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And also from information we know the number of calls necessary to call a sales and the average and also the direct selling expenses are budgeted to be as percent of two sales. 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That means the customers under the break-even, should we afford them or not concerned of them? The answer is not because first growing customers and growing sales. 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Next slide. Break-even account analysis provides a starting place from which to determine the minimum size account should be called in. 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How much time should I take for a specific prospect? 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This, to mention it, these methods are selling factors model, profit models, decision models, and sales process models. Thank you. Listen. Thanks, Saham. Now, we would like to sum up what Saham introduced to us. 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Now, last class we studied two approaches which is going to provide us with signals if our sales agency is winning and gaining profit or it is losing by calculating call bear cost", "tokens": [2212, 337, 5474, 420, 15005, 13, 823, 11, 1036, 1508, 321, 9454, 732, 11587, 597, 307, 516, 281, 2893, 505, 365, 12354, 498, 527, 5763, 7934, 307, 8224, 293, 19752, 7475, 420, 309, 307, 7027, 538, 28258, 818, 6155, 2063], "avg_logprob": -0.22694359465343197, "compression_ratio": 1.452054794520548, "no_speech_prob": 0.0, "words": [{"start": 501.0, "end": 501.82, "word": " given", "probability": 0.77490234375}, {"start": 501.82, "end": 502.64, "word": " for", "probability": 0.7001953125}, {"start": 502.64, "end": 504.72, "word": " customer", "probability": 0.4443359375}, {"start": 504.72, "end": 506.72, "word": " or", "probability": 0.78955078125}, {"start": 506.72, "end": 507.14, "word": " prospect.", "probability": 0.63037109375}, {"start": 511.02, "end": 511.62, "word": " Now,", "probability": 0.8505859375}, {"start": 512.22, "end": 513.3, "word": " last", "probability": 0.80810546875}, {"start": 513.3, "end": 513.74, "word": " class", "probability": 0.9658203125}, {"start": 513.74, "end": 514.02, "word": " we", "probability": 0.6474609375}, {"start": 514.02, "end": 514.56, "word": " studied", "probability": 0.931640625}, {"start": 514.56, "end": 516.12, "word": " two", "probability": 0.8896484375}, {"start": 516.12, "end": 516.62, "word": " approaches", "probability": 0.7802734375}, {"start": 516.62, "end": 518.16, "word": " which", "probability": 0.6708984375}, {"start": 518.16, "end": 518.4, "word": " is", "probability": 0.791015625}, {"start": 518.4, "end": 518.68, "word": " going", "probability": 0.94482421875}, {"start": 518.68, "end": 518.92, "word": " to", "probability": 0.970703125}, {"start": 518.92, "end": 519.28, "word": " provide", "probability": 0.95751953125}, {"start": 519.28, "end": 519.52, "word": " us", "probability": 0.9462890625}, {"start": 519.52, "end": 519.72, "word": " with", "probability": 0.892578125}, {"start": 519.72, "end": 520.24, "word": " signals", "probability": 0.97607421875}, {"start": 520.24, "end": 521.38, "word": " if", "probability": 0.78125}, {"start": 521.38, "end": 521.66, "word": " our", "probability": 0.8955078125}, {"start": 521.66, "end": 521.88, "word": " sales", "probability": 0.94384765625}, {"start": 521.88, "end": 522.24, "word": " agency", "probability": 0.90966796875}, {"start": 522.24, "end": 522.56, "word": " is", "probability": 0.951171875}, {"start": 522.56, "end": 522.84, "word": " winning", "probability": 0.95556640625}, {"start": 522.84, "end": 523.24, "word": " and", "probability": 0.9140625}, {"start": 523.24, "end": 523.48, "word": " gaining", "probability": 0.84521484375}, {"start": 523.48, "end": 523.78, "word": " profit", "probability": 0.7412109375}, {"start": 523.78, "end": 524.12, "word": " or", "probability": 0.8564453125}, {"start": 524.12, "end": 524.28, "word": " it", "probability": 0.86328125}, {"start": 524.28, "end": 524.42, "word": " is", "probability": 0.931640625}, {"start": 524.42, "end": 524.8, "word": " losing", "probability": 0.90771484375}, {"start": 524.8, "end": 526.16, "word": " by", "probability": 0.496826171875}, {"start": 526.16, "end": 526.84, "word": " calculating", "probability": 0.91650390625}, {"start": 526.84, "end": 527.6, "word": " call", "probability": 0.443359375}, {"start": 527.6, "end": 527.76, "word": " bear", "probability": 0.58154296875}, {"start": 527.76, "end": 528.18, "word": " cost", "probability": 0.88232421875}], "temperature": 1.0}, {"id": 21, "seek": 55434, "start": 528.98, "end": 554.34, "text": " or cost per call and the second one by calculating the break-even sales volume. Those two approaches or these two approaches they cannot provide us with any information about the time which we should spend as a sales agency or as a salesperson with the prospect or the customer. 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Listen here, if the time which I am going to invest in this customer is wasteless, counterproductive, this means that the sales agency is losing. The same thing if I am going to make in vain phone calls, in vain means useless, equivalent to the word useless.", "tokens": [8595, 264, 1230, 295, 2593, 5498, 597, 286, 820, 5502, 337, 1184, 15005, 13, 7501, 510, 11, 498, 264, 565, 597, 286, 669, 516, 281, 1963, 294, 341, 5474, 307, 49075, 4272, 11, 5682, 14314, 20221, 11, 341, 1355, 300, 264, 5763, 7934, 307, 7027, 13, 440, 912, 551, 498, 286, 669, 516, 281, 652, 294, 22240, 2593, 5498, 11, 294, 22240, 1355, 14115, 11, 10344, 281, 264, 1349, 14115, 13], "avg_logprob": -0.2014973999725448, "compression_ratio": 1.6616915422885572, "no_speech_prob": 0.0, "words": [{"start": 555.26, "end": 555.86, "word": " regarding", "probability": 0.4833984375}, {"start": 555.86, "end": 556.26, "word": " the", "probability": 0.912109375}, {"start": 556.26, "end": 556.56, "word": " number", "probability": 0.90087890625}, {"start": 556.56, "end": 556.78, "word": " of", "probability": 0.9658203125}, {"start": 556.78, "end": 557.18, "word": " phone", 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Therefore, so far we do not have clear-cut information about what are the boundaries or the borders according to which the salesperson should act and work with the customers. Let's paraphrase what we are saying. What we would like to say is the following.", "tokens": [759, 286, 478, 516, 281, 2593, 294, 22240, 264, 4581, 1553, 1419, 746, 1219, 3353, 5824, 490, 552, 11, 611, 321, 366, 7027, 13, 7504, 11, 370, 1400, 321, 360, 406, 362, 1850, 12, 6672, 1589, 466, 437, 366, 264, 13180, 420, 264, 16287, 4650, 281, 597, 264, 5763, 10813, 820, 605, 293, 589, 365, 264, 4581, 13, 961, 311, 36992, 1703, 651, 437, 321, 366, 1566, 13, 708, 321, 576, 411, 281, 584, 307, 264, 3480, 13], "avg_logprob": -0.14556962628907796, "compression_ratio": 1.6493506493506493, "no_speech_prob": 0.0, "words": [{"start": 586.07, "end": 586.33, "word": " If", "probability": 0.724609375}, {"start": 586.33, "end": 586.53, "word": " I'm", "probability": 0.79248046875}, {"start": 586.53, "end": 586.81, "word": " going", "probability": 0.92578125}, {"start": 586.81, "end": 587.03, "word": " to", "probability": 0.96484375}, {"start": 587.03, "end": 587.29, "word": " phone", "probability": 0.94287109375}, {"start": 587.29, "end": 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The answer is no. Because if you are talking about one perfect model, then why should we bother ourselves and study more than one model? Okay, so let's start with the first model, single factor. 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I want to complete what Saham mentioned about the customer. 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Just that. Thanks. Okay. My name originally Nabil. Hello, studied... Nabil, it's okay. 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I studied business administration at third level. Today I'm going to talk about the single factor model, which is one of the four setting account properties. The single factor model is from accounting perspective. From accounting perspective means it's a model of security return that acknowledge", "tokens": [1222, 1315, 307, 316, 701, 73, 426, 5177, 6128, 263, 13, 286, 9454, 1606, 7236, 412, 2636, 1496, 13, 2692, 286, 478, 516, 281, 751, 466, 264, 2167, 5952, 2316, 11, 597, 307, 472, 295, 264, 1451, 3287, 2696, 7221, 13, 440, 2167, 5952, 2316, 307, 490, 19163, 4585, 13, 3358, 19163, 4585, 1355, 309, 311, 257, 2316, 295, 3825, 2736, 300, 10692], "avg_logprob": -0.3386230459436774, "compression_ratio": 1.618811881188119, "no_speech_prob": 1.4901161193847656e-05, "words": [{"start": 912.18, "end": 912.32, "word": " My", "probability": 0.72802734375}, {"start": 912.32, "end": 912.56, "word": " name", "probability": 0.87890625}, {"start": 912.56, "end": 912.7, "word": " is", "probability": 0.3955078125}, {"start": 912.7, "end": 912.86, "word": " Areej", "probability": 0.4138997395833333}, {"start": 912.86, "end": 913.16, "word": " Nabil", "probability": 0.6673583984375}, {"start": 913.16, "end": 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They're a company Tom's Cook for travel. Divide a group into three customers. We have A and B and C. Which customer? Sorry, according to the single factor model I read, which customer from A or B or C should be given the highest priority?", "tokens": [440, 4274, 5763, 5523, 680, 1618, 13, 814, 434, 257, 2237, 5041, 311, 12259, 337, 3147, 13, 9886, 482, 257, 1594, 666, 1045, 4581, 13, 492, 362, 316, 293, 363, 293, 383, 13, 3013, 5474, 30, 4919, 11, 4650, 281, 264, 2167, 5952, 2316, 286, 1401, 11, 597, 5474, 490, 316, 420, 363, 420, 383, 820, 312, 2212, 264, 6343, 9365, 30], "avg_logprob": -0.3025793698098924, "compression_ratio": 1.4397905759162304, "no_speech_prob": 1.1920928955078125e-07, "words": [{"start": 1060.66, "end": 1060.96, "word": " The", "probability": 0.48486328125}, {"start": 1060.96, "end": 1061.4, "word": " average", "probability": 0.72314453125}, {"start": 1061.4, "end": 1061.82, "word": " sales", "probability": 0.9140625}, {"start": 1061.82, "end": 1062.24, "word": " volume", "probability": 0.61669921875}, {"start": 1062.24, "end": 1062.52, "word": " per", "probability": 0.95654296875}, {"start": 1062.52, "end": 1062.98, "word": " month.", "probability": 0.92431640625}, {"start": 1063.88, "end": 1064.12, "word": " They're", "probability": 0.46343994140625}, {"start": 1064.12, "end": 1064.6, "word": " a", "probability": 0.81005859375}, {"start": 1064.6, "end": 1065.12, "word": " company", "probability": 0.90966796875}, {"start": 1065.12, "end": 1066.06, "word": " Tom's", "probability": 0.51025390625}, {"start": 1066.06, "end": 1066.36, "word": " Cook", "probability": 0.4267578125}, {"start": 1066.36, "end": 1066.84, "word": " for", "probability": 0.521484375}, {"start": 1066.84, "end": 1067.36, "word": " travel.", "probability": 0.74267578125}, {"start": 1069.1, "end": 1069.78, "word": " Divide", "probability": 0.897216796875}, {"start": 1069.78, "end": 1070.12, "word": " a", "probability": 0.443115234375}, {"start": 1070.12, "end": 1070.86, "word": " group", "probability": 0.970703125}, {"start": 1070.86, "end": 1071.18, "word": " into", "probability": 0.8486328125}, {"start": 1071.18, "end": 1071.62, "word": " three", "probability": 0.89306640625}, {"start": 1071.62, "end": 1072.9, "word": " customers.", "probability": 0.857421875}, {"start": 1073.54, "end": 1073.8, "word": " We", "probability": 0.9423828125}, {"start": 1073.8, "end": 1074.14, "word": " have", "probability": 0.9501953125}, {"start": 1074.14, "end": 1074.5, "word": " A", "probability": 0.95703125}, {"start": 1074.5, "end": 1074.88, "word": " and", "probability": 0.7451171875}, {"start": 1074.88, "end": 1075.08, "word": " B", "probability": 0.99560546875}, {"start": 1075.08, "end": 1075.4, "word": " and", "probability": 0.93701171875}, {"start": 1075.4, "end": 1075.62, "word": " C.", "probability": 0.99853515625}, {"start": 1079.66, "end": 1080.2, "word": " Which", "probability": 0.415771484375}, {"start": 1080.2, "end": 1080.62, "word": " customer?", "probability": 0.68505859375}, {"start": 1080.62, "end": 1080.8, "word": " Sorry,", "probability": 0.33984375}, {"start": 1080.9, "end": 1081.4, "word": " according", "probability": 0.9296875}, {"start": 1081.4, "end": 1081.62, "word": " to", "probability": 0.96435546875}, {"start": 1081.62, "end": 1081.74, "word": " the", "probability": 0.89306640625}, {"start": 1081.74, "end": 1081.98, "word": " single", "probability": 0.81201171875}, {"start": 1081.98, "end": 1082.26, "word": " factor", "probability": 0.740234375}, {"start": 1082.26, "end": 1082.5, "word": " model", "probability": 0.89013671875}, {"start": 1082.5, "end": 1082.72, "word": " I", "probability": 0.1572265625}, {"start": 1082.72, "end": 1082.92, "word": " read,", "probability": 0.88525390625}, {"start": 1083.16, "end": 1083.98, "word": " which", "probability": 0.92724609375}, {"start": 1083.98, "end": 1084.5, "word": " customer", "probability": 0.78125}, {"start": 1084.5, "end": 1084.88, "word": " from", "probability": 0.869140625}, {"start": 1084.88, "end": 1085.36, "word": " A", "probability": 0.98974609375}, {"start": 1085.36, "end": 1085.64, "word": " or", "probability": 0.9091796875}, {"start": 1085.64, "end": 1085.96, "word": " B", "probability": 0.99853515625}, {"start": 1085.96, "end": 1086.24, "word": " or", "probability": 0.96240234375}, {"start": 1086.24, "end": 1086.56, "word": " C", "probability": 0.9990234375}, {"start": 1086.56, "end": 1087.24, "word": " should", "probability": 0.90478515625}, {"start": 1087.24, "end": 1087.44, "word": " be", "probability": 0.95654296875}, {"start": 1087.44, "end": 1087.7, "word": " given", "probability": 0.90380859375}, {"start": 1087.7, "end": 1087.9, "word": " the", "probability": 0.91796875}, {"start": 1087.9, "end": 1088.16, "word": " highest", "probability": 0.9599609375}, {"start": 1088.16, "end": 1088.6, "word": " priority?", "probability": 0.9541015625}], "temperature": 1.0}, {"id": 44, "seek": 111809, "start": 1092.3, "end": 1118.1, "text": " Which gain high profit or high revenue? So, the answer is going to be C. C. Yes, it won. Why? Because his total revenue is the highest one between A and B. Okay. That's it? Yes. Thank you very much. Big clap for him. 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Why we are naming it to be the traditional model? Because this model is very narrow and it is focusing or zooming on one variable or characteristics, which is the sales volume. I think Sam, she indicated at the very beginning of the class, what did she say? 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In other words, the size or the big volume isn't always equivalent to the profit or to the sales growth. 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which is called sales volume. We are talking about other things. We are talking about other things. As Arif said, in this model, imagine this is considered to be customer number C, this is customer number A and this is customer number B. According to the ideology of the single factor model, they will say customer number C is considered to be priority number one.", "tokens": [1177, 380, 2681, 264, 1349, 597, 307, 1219, 5763, 5523, 13, 492, 366, 1417, 466, 661, 721, 13, 492, 366, 1417, 466, 661, 721, 13, 1018, 1587, 351, 848, 11, 294, 341, 2316, 11, 3811, 341, 307, 4888, 281, 312, 5474, 1230, 383, 11, 341, 307, 5474, 1230, 316, 293, 341, 307, 5474, 1230, 363, 13, 7328, 281, 264, 23101, 295, 264, 2167, 5952, 2316, 11, 436, 486, 584, 5474, 1230, 383, 307, 4888, 281, 312, 9365, 1230, 472, 13], "avg_logprob": -0.2019675867057141, "compression_ratio": 1.9595959595959596, "no_speech_prob": 0.0, "words": [{"start": 1227.3200000000002, "end": 1227.94, "word": " doesn't", "probability": 0.6944580078125}, {"start": 1227.94, "end": 1228.34, "word": " equal", "probability": 0.88232421875}, {"start": 1228.34, "end": 1229.06, "word": " the", "probability": 0.8125}, {"start": 1229.06, "end": 1229.5, "word": " word", "probability": 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to", "probability": 0.943359375}, {"start": 1253.14, "end": 1253.36, "word": " be", "probability": 0.94775390625}, {"start": 1253.36, "end": 1254.0, "word": " priority", "probability": 0.94189453125}, {"start": 1254.0, "end": 1254.52, "word": " number", "probability": 0.9267578125}, {"start": 1254.52, "end": 1254.84, "word": " one.", "probability": 0.59716796875}], "temperature": 1.0}, {"id": 50, "seek": 128464, "start": 1256.02, "end": 1284.64, "text": " P it refers to priority. But number A is going to be priority number two and number B or the letter B it refers to priority number three. Simply why? Because this model is forgetting everything about the customer or the prospect and we are focusing on one thing which is exactly the sales volume which he is providing for us as a sales agent.", "tokens": [430, 309, 14942, 281, 9365, 13, 583, 1230, 316, 307, 516, 281, 312, 9365, 1230, 732, 293, 1230, 363, 420, 264, 5063, 363, 309, 14942, 281, 9365, 1230, 1045, 13, 19596, 983, 30, 1436, 341, 2316, 307, 25428, 1203, 466, 264, 5474, 420, 264, 15005, 293, 321, 366, 8416, 322, 472, 551, 597, 307, 2293, 264, 5763, 5523, 597, 415, 307, 6530, 337, 505, 382, 257, 5763, 9461, 13], "avg_logprob": -0.20078125170298985, "compression_ratio": 1.715, "no_speech_prob": 5.960464477539063e-08, "words": [{"start": 1256.02, "end": 1256.42, "word": " P", "probability": 0.634765625}, {"start": 1256.42, "end": 1257.02, "word": " it", "probability": 0.32470703125}, {"start": 1257.02, "end": 1257.3, "word": " refers", "probability": 0.84716796875}, {"start": 1257.3, "end": 1257.64, "word": " to", "probability": 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Clear? But as we said, this isn't always reflecting the actual circumstances of the customer or the prospect.", "tokens": [759, 341, 307, 264, 10270, 11, 341, 1355, 5474, 1230, 383, 307, 4888, 281, 312, 264, 881, 8263, 472, 293, 264, 881, 1021, 472, 293, 820, 312, 2212, 2857, 565, 293, 2857, 4630, 322, 264, 2696, 295, 15005, 1230, 316, 293, 15005, 1230, 363, 13, 14993, 30, 583, 382, 321, 848, 11, 341, 1943, 380, 1009, 23543, 264, 3539, 9121, 295, 264, 5474, 420, 264, 15005, 13], "avg_logprob": -0.13809742318356738, "compression_ratio": 1.69, "no_speech_prob": 5.960464477539063e-08, "words": [{"start": 1285.89, "end": 1286.19, "word": " If", "probability": 0.83349609375}, {"start": 1286.19, "end": 1286.43, "word": " this", "probability": 0.919921875}, {"start": 1286.43, "end": 1286.57, "word": " is", "probability": 0.9404296875}, {"start": 1286.57, "end": 1286.75, "word": " the", "probability": 0.8994140625}, {"start": 1286.75, "end": 1287.07, "word": " margin,", "probability": 0.91845703125}, 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Because we are talking about first long-term relationship with the customer, the quality or the nature of the items or the products which are purchased by this customer. All these things are marginalized or forgotten or are not considered by the first model. Therefore, the first model is considered to be imperfect with huge criticism were forwarded against it. Okay?", "tokens": [1545, 30, 1436, 321, 366, 1417, 466, 700, 938, 12, 7039, 2480, 365, 264, 5474, 11, 264, 3125, 420, 264, 3687, 295, 264, 4754, 420, 264, 3383, 597, 366, 14734, 538, 341, 5474, 13, 1057, 613, 721, 366, 32522, 420, 11832, 420, 366, 406, 4888, 538, 264, 700, 2316, 13, 7504, 11, 264, 700, 2316, 307, 4888, 281, 312, 26714, 365, 2603, 15835, 645, 2128, 292, 1970, 309, 13, 1033, 30], "avg_logprob": -0.20681423197189966, "compression_ratio": 1.626086956521739, "no_speech_prob": 5.960464477539063e-08, "words": [{"start": 1313.21, "end": 1313.65, "word": " Why?", "probability": 0.70458984375}, {"start": 1314.35, "end": 1314.85, "word": " Because", "probability": 0.84375}, {"start": 1314.85, "end": 1314.99, "word": " we", "probability": 0.86376953125}, {"start": 1314.99, "end": 1315.11, "word": " are", "probability": 0.84814453125}, {"start": 1315.11, "end": 1315.39, "word": " talking", "probability": 0.8388671875}, {"start": 1315.39, "end": 1315.93, "word": " about", 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Now we'll talk about the portfolio model or the second model to give priorities to the customers. The portfolio model is designed to overcome the shortcomings of the first single factor model, that is it depends or it considers just the sales volume. 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We have multiple factors to determine which is our attractive customer and which is the least attractive, which is moderately attractive. Figure 3-2 straight one will know portfolio model. 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I mean the relationship between the company and the customer. If there is a good relation or if there is a bad relation, and the attitude of the customer towards our product, and the portion of his sales purchased from our products, margin of profit of our product. 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The core accounts are the most basic accounts according to this model. And we have high account opportunity. By this, we mean that he need or he will be in a bad need for our customers today or in the future. And in the competitive position, if it's strong, we have a high portion of his purchases. And we have a high profit margin from his purchases. Accounts are very attractive. 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The second category is growth accounts. 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We have a weak competitive position where he may have a bad attitude toward our products or may he not purchasing from us. So they are called growth accounts because we have high opportunity. They may have a high, they may have or they may need our products in the future. So they are called the growth accounts. In the future they may grow and they may need our products more. So accounts are potentially attractive, not completely attractive, may want to invest in heavily. 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We must here invest. The third category is drag accounts. 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He may have purchased from us, but it's not so high. We may not profit. He is not lucrative for us. And he have low opportunity. We may not have the product that can satisfy his needs. So he didn't need our products. Accounts are moderately attractive. And we should invest enough to maintain current position. 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Yes. We should maintain our status quo. After that, if we have exerted more effort or time or cost, we will be in loss. The fourth category is problem accounts. Problem accounts, they have bad signs from the two factors. 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There is consultative type and also there is transactional type and there is enterprise type. This model, do you think that it focuses on transactional or consultative or the last one is enterprise? Consultative. Why? 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Long relation. Yes, what else? We talk about relatively long relation.", "tokens": [2743, 570, 295, 437, 30, 8282, 9721, 13, 1079, 11, 437, 1646, 30, 492, 751, 466, 7226, 938, 9721, 13], "avg_logprob": -0.27604165531340097, "compression_ratio": 1.1481481481481481, "no_speech_prob": 0.0, "words": [{"start": 1647.42, "end": 1647.82, "word": " Also", "probability": 0.48486328125}, {"start": 1647.82, "end": 1648.18, "word": " because", "probability": 0.81884765625}, {"start": 1648.18, "end": 1648.36, "word": " of", "probability": 0.953125}, {"start": 1648.36, "end": 1648.64, "word": " what?", "probability": 0.85009765625}, {"start": 1649.16, "end": 1649.4, "word": " Long", "probability": 0.83740234375}, {"start": 1649.4, "end": 1649.82, "word": " relation.", "probability": 0.5634765625}, {"start": 1656.02, "end": 1656.84, "word": " Yes,", "probability": 0.328125}, {"start": 1662.12, "end": 1662.56, "word": " what", "probability": 0.9111328125}, {"start": 1662.56, "end": 1662.9, "word": " else?", "probability": 0.92822265625}, {"start": 1663.68, "end": 1663.84, "word": " We", "probability": 0.93603515625}, {"start": 1663.84, "end": 1664.02, "word": " talk", "probability": 0.66357421875}, {"start": 1664.02, "end": 1664.4, "word": " about", "probability": 0.9013671875}, {"start": 1664.4, "end": 1665.04, "word": " relatively", "probability": 0.8193359375}, {"start": 1665.04, "end": 1666.5, "word": " long", "probability": 0.86376953125}, {"start": 1666.5, "end": 1666.98, "word": " relation.", "probability": 0.884765625}], "temperature": 1.0}, {"id": 69, "seek": 170019, "start": 1672.13, "end": 1700.19, "text": " Also, we talk about somewhat high investment. Here we have invest heavily, and here we will invest, and here we will invest, but here we will not invest. So there is high investment from both parties, the customer and the supplier, so that it's relatively consultative type. Thank you. Thank you very much for sitting. Now, if you are going to rank him according to priority or according to the importance, how are you going to rank him?", "tokens": [2743, 11, 321, 751, 466, 8344, 1090, 6078, 13, 1692, 321, 362, 1963, 10950, 11, 293, 510, 321, 486, 1963, 11, 293, 510, 321, 486, 1963, 11, 457, 510, 321, 486, 406, 1963, 13, 407, 456, 307, 1090, 6078, 490, 1293, 8265, 11, 264, 5474, 293, 264, 31909, 11, 370, 300, 309, 311, 7226, 7189, 1166, 2010, 13, 1044, 291, 13, 1044, 291, 588, 709, 337, 3798, 13, 823, 11, 498, 291, 366, 516, 281, 6181, 796, 4650, 281, 9365, 420, 4650, 281, 264, 7379, 11, 577, 366, 291, 516, 281, 6181, 796, 30], "avg_logprob": -0.22253288940379495, "compression_ratio": 1.8559322033898304, "no_speech_prob": 1.7881393432617188e-07, "words": [{"start": 1672.13, "end": 1672.57, "word": " Also,", "probability": 0.833984375}, {"start": 1672.67, "end": 1673.09, "word": " we", "probability": 0.90283203125}, {"start": 1673.09, "end": 1673.33, "word": " talk", "probability": 0.7294921875}, 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Would you mind to clean the board? And try to number them. One is the most important, four is the least important. Yes. This one is going to be intended for number one. Core accounts will have priority number one, and growth accounts will have priority number two, drag accounts will have priority number three, and this is four. Invest enough to maintain current customers, but there we will invest more.", "tokens": [639, 307, 264, 9365, 1230, 472, 13, 6068, 291, 1575, 281, 2541, 264, 3150, 30, 400, 853, 281, 1230, 552, 13, 1485, 307, 264, 881, 1021, 11, 1451, 307, 264, 1935, 1021, 13, 1079, 13, 639, 472, 307, 516, 281, 312, 10226, 337, 1230, 472, 13, 14798, 9402, 486, 362, 9365, 1230, 472, 11, 293, 4599, 9402, 486, 362, 9365, 1230, 732, 11, 5286, 9402, 486, 362, 9365, 1230, 1045, 11, 293, 341, 307, 1451, 13, 14008, 1547, 281, 6909, 2190, 4581, 11, 457, 456, 321, 486, 1963, 544, 13], "avg_logprob": -0.21892170820917403, "compression_ratio": 1.9598214285714286, "no_speech_prob": 5.960464477539063e-08, "words": [{"start": 1700.86, "end": 1701.16, "word": " This", "probability": 0.5576171875}, {"start": 1701.16, "end": 1701.28, "word": " is", "probability": 0.92578125}, {"start": 1701.28, "end": 1701.36, "word": " the", "probability": 0.31396484375}, {"start": 1701.36, "end": 1701.58, "word": " priority", 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We are discussing and thinking in a very loud voice. In number two, we are talking about a very high level of risk, a very high level of probability. But in number three, we do not have such risk. We do not have such wide range of probability. 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Other points? Thanks. I appreciate your time. Time to remember. Once again, before two weeks or three weeks, we were talking about what is the biggest resource by which we can ensure a sales agency growth or a sales agency profitability. We said maintaining our current customers. 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It might be very risky. And later on, we might not invest at all. But here, we are playing on the playground which is familiar to us. Therefore, we will continue working as we used to work in the past. 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"text": " Are you convinced now Haneen? Yes. Any question, any comments about this? This is important, very important. Gone. Now we are going to go to the second model which is called decision.", "tokens": [2014, 291, 12561, 586, 389, 1929, 268, 30, 1079, 13, 2639, 1168, 11, 604, 3053, 466, 341, 30, 639, 307, 1021, 11, 588, 1021, 13, 39068, 13, 823, 321, 366, 516, 281, 352, 281, 264, 1150, 2316, 597, 307, 1219, 3537, 13], "avg_logprob": -0.3028706339902656, "compression_ratio": 1.3049645390070923, "no_speech_prob": 0.0, "words": [{"start": 1895.31, "end": 1895.51, "word": " Are", "probability": 0.70068359375}, {"start": 1895.51, "end": 1895.63, "word": " you", "probability": 0.96484375}, {"start": 1895.63, "end": 1895.95, "word": " convinced", "probability": 0.91015625}, {"start": 1895.95, "end": 1896.11, "word": " now", "probability": 0.8642578125}, {"start": 1896.11, "end": 1896.43, "word": " Haneen?", "probability": 0.6894124348958334}, {"start": 1896.73, "end": 1896.99, "word": " Yes.", "probability": 0.63134765625}, {"start": 1898.53, "end": 1898.71, "word": " Any", "probability": 0.6396484375}, {"start": 1898.71, "end": 1899.07, "word": " question,", "probability": 0.45458984375}, {"start": 1899.21, "end": 1899.29, "word": " any", "probability": 0.8974609375}, {"start": 1899.29, "end": 1899.55, "word": " comments", "probability": 0.62939453125}, {"start": 1899.55, "end": 1899.81, "word": " about", "probability": 0.87353515625}, {"start": 1899.81, "end": 1900.07, "word": " this?", "probability": 0.931640625}, {"start": 1900.71, "end": 1901.23, "word": " This", "probability": 0.63623046875}, {"start": 1901.23, "end": 1901.43, "word": " is", "probability": 0.94775390625}, {"start": 1901.43, "end": 1901.97, "word": " important,", "probability": 0.837890625}, {"start": 1902.85, "end": 1903.23, "word": " very", "probability": 0.85400390625}, {"start": 1903.23, "end": 1903.77, "word": " important.", "probability": 0.8798828125}, {"start": 1905.17, "end": 1905.57, "word": " Gone.", "probability": 0.6484375}, {"start": 1906.93, "end": 1907.25, "word": " Now", "probability": 0.94921875}, {"start": 1907.25, "end": 1907.43, "word": " we", "probability": 0.7822265625}, {"start": 1907.43, "end": 1907.55, "word": " are", "probability": 0.89794921875}, {"start": 1907.55, "end": 1907.73, "word": " going", "probability": 0.947265625}, {"start": 1907.73, "end": 1907.89, "word": " to", "probability": 0.9658203125}, {"start": 1907.89, "end": 1908.03, "word": " go", "probability": 0.88134765625}, {"start": 1908.03, "end": 1908.15, "word": " to", "probability": 0.9365234375}, {"start": 1908.15, "end": 1908.31, "word": " the", "probability": 0.9091796875}, {"start": 1908.31, "end": 1908.55, "word": " second", "probability": 0.890625}, {"start": 1908.55, "end": 1908.79, "word": " model", "probability": 0.68896484375}, {"start": 1908.79, "end": 1908.95, "word": " which", "probability": 0.74365234375}, {"start": 1908.95, "end": 1909.07, "word": " is", "probability": 0.94921875}, {"start": 1909.07, "end": 1909.49, "word": " called", "probability": 0.8759765625}, {"start": 1909.49, "end": 1910.43, "word": " decision.", "probability": 0.54052734375}], "temperature": 1.0}, {"id": 78, "seek": 193756, "start": 1911.4, "end": 1937.56, "text": " I am Emanek Hel, studied business administration at level three. I will talk about decision model. Decision model applied to overcome some shortcomings happened in the previous model. So we discussed the shortcomings. We have two shortcomings. 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This is a chart discussed the relation between number of sales call per quarters and the dollar sale per quarter. 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One call, for example, for the customer will not provide us with this volume. Yes, exactly.", "tokens": [1171, 1365, 11, 562, 321, 652, 264, 700, 5763, 818, 337, 264, 472, 5474, 11, 321, 500, 380, 915, 544, 4134, 295, 264, 5474, 382, 510, 13, 1485, 818, 11, 337, 1365, 11, 337, 264, 5474, 486, 406, 2893, 505, 365, 341, 5523, 13, 1079, 11, 2293, 13], "avg_logprob": -0.2788584183673469, "compression_ratio": 1.4965034965034965, "no_speech_prob": 1.7881393432617188e-07, "words": [{"start": 2014.53, "end": 2014.75, "word": " For", "probability": 0.611328125}, {"start": 2014.75, "end": 2015.11, "word": " example,", "probability": 0.97265625}, {"start": 2015.31, "end": 2015.37, "word": " when", "probability": 0.91552734375}, {"start": 2015.37, "end": 2015.53, "word": " we", "probability": 0.9560546875}, {"start": 2015.53, "end": 2018.15, "word": " make", "probability": 0.7158203125}, {"start": 2018.15, "end": 2018.33, "word": " the", "probability": 0.88818359375}, {"start": 2018.33, "end": 2018.65, "word": " first", "probability": 0.8798828125}, {"start": 2018.65, "end": 2019.63, "word": " sales", "probability": 0.84912109375}, {"start": 2019.63, "end": 2019.93, "word": " call", "probability": 0.88232421875}, {"start": 2019.93, "end": 2020.17, "word": " for", "probability": 0.83056640625}, {"start": 2020.17, "end": 2020.31, "word": " the", "probability": 0.51953125}, {"start": 2020.31, "end": 2020.47, "word": " one", "probability": 0.93212890625}, {"start": 2020.47, "end": 2020.89, "word": " customer,", "probability": 0.7841796875}, {"start": 2021.45, "end": 2021.57, "word": " we", "probability": 0.92333984375}, {"start": 2021.57, "end": 2022.39, "word": " don't", "probability": 0.9375}, {"start": 2022.39, "end": 2022.71, "word": " find", "probability": 0.64697265625}, {"start": 2022.71, "end": 2023.59, "word": " more", "probability": 0.5986328125}, {"start": 2023.59, "end": 2024.97, "word": " response", "probability": 0.8818359375}, {"start": 2024.97, "end": 2025.23, "word": " of", "probability": 0.708984375}, {"start": 2025.23, "end": 2025.45, "word": " the", "probability": 0.90234375}, {"start": 2025.45, "end": 2025.95, "word": " customer", "probability": 0.712890625}, {"start": 2025.95, "end": 2026.73, "word": " as", "probability": 0.5859375}, {"start": 2026.73, "end": 2027.05, "word": " here.", "probability": 0.814453125}, {"start": 2028.49, "end": 2028.69, "word": " One", "probability": 0.78857421875}, {"start": 2028.69, "end": 2028.97, "word": " call,", "probability": 0.89306640625}, {"start": 2029.09, "end": 2029.11, "word": " for", "probability": 0.95458984375}, {"start": 2029.11, "end": 2030.67, "word": " example,", "probability": 0.9755859375}, {"start": 2030.79, "end": 2030.95, "word": " for", "probability": 0.68798828125}, {"start": 2030.95, "end": 2031.13, "word": " the", "probability": 0.90869140625}, {"start": 2031.13, "end": 2031.53, "word": " customer", "probability": 0.779296875}, {"start": 2031.53, "end": 2032.17, "word": " will", "probability": 0.7421875}, {"start": 2032.17, "end": 2032.67, "word": " not", "probability": 0.74755859375}, {"start": 2032.67, "end": 2033.55, "word": " provide", "probability": 0.54248046875}, {"start": 2033.55, "end": 2033.87, "word": " us", "probability": 0.8935546875}, {"start": 2033.87, "end": 2034.15, "word": " with", "probability": 0.82080078125}, {"start": 2034.15, "end": 2034.47, "word": " this", "probability": 0.83447265625}, {"start": 2034.47, "end": 2035.21, "word": " volume.", "probability": 0.54345703125}, {"start": 2036.15, "end": 2036.79, "word": " Yes,", "probability": 0.8037109375}, {"start": 2036.89, "end": 2037.05, "word": " exactly.", "probability": 0.8837890625}], "temperature": 1.0}, {"id": 84, "seek": 205759, "start": 2037.47, "end": 2057.59, "text": " We don't find much response of the customer. So we make another call for the customer. Here we will face the little increase of the response of the customer. And the third, we will call from three to four calling.", "tokens": [492, 500, 380, 915, 709, 4134, 295, 264, 5474, 13, 407, 321, 652, 1071, 818, 337, 264, 5474, 13, 1692, 321, 486, 1851, 264, 707, 3488, 295, 264, 4134, 295, 264, 5474, 13, 400, 264, 2636, 11, 321, 486, 818, 490, 1045, 281, 1451, 5141, 13], "avg_logprob": -0.3392619592078189, "compression_ratio": 1.646153846153846, "no_speech_prob": 3.337860107421875e-06, "words": [{"start": 2037.47, "end": 2037.85, "word": " We", "probability": 0.442138671875}, {"start": 2037.85, "end": 2038.25, "word": " don't", "probability": 0.6986083984375}, {"start": 2038.25, "end": 2038.59, "word": " find", "probability": 0.69775390625}, {"start": 2038.59, "end": 2038.95, "word": " much", "probability": 0.82373046875}, {"start": 2038.95, "end": 2039.37, "word": " response", "probability": 0.9462890625}, {"start": 2039.37, "end": 2039.59, "word": " of", "probability": 0.51806640625}, {"start": 2039.59, "end": 2039.89, "word": " the", "probability": 0.89990234375}, {"start": 2039.89, "end": 2041.79, "word": " customer.", "probability": 0.466064453125}, {"start": 2043.19, "end": 2043.85, "word": " So", "probability": 0.70166015625}, {"start": 2043.85, "end": 2044.09, "word": " we", "probability": 0.69091796875}, {"start": 2044.09, "end": 2044.31, "word": " make", "probability": 0.8603515625}, {"start": 2044.31, "end": 2044.79, "word": " another", "probability": 0.8818359375}, {"start": 2044.79, "end": 2045.99, "word": " call", "probability": 0.88671875}, {"start": 2045.99, "end": 2046.37, "word": " for", "probability": 0.59912109375}, {"start": 2046.37, "end": 2046.53, "word": " the", "probability": 0.90087890625}, {"start": 2046.53, "end": 2046.81, "word": " customer.", "probability": 0.75390625}, {"start": 2047.01, "end": 2047.15, "word": " Here", "probability": 0.53125}, {"start": 2047.15, "end": 2047.33, "word": " we", "probability": 0.7451171875}, {"start": 2047.33, "end": 2047.59, "word": " will", "probability": 0.58447265625}, {"start": 2047.59, "end": 2047.91, "word": " face", "probability": 0.87646484375}, {"start": 2047.91, "end": 2048.15, "word": " the", "probability": 0.7294921875}, {"start": 2048.15, "end": 2048.45, "word": " little", "probability": 0.84130859375}, {"start": 2048.45, "end": 2048.89, "word": " increase", "probability": 0.828125}, {"start": 2048.89, "end": 2049.11, "word": " of", "probability": 0.8369140625}, {"start": 2049.11, "end": 2049.29, "word": " the", "probability": 0.759765625}, {"start": 2049.29, "end": 2049.65, "word": " response", "probability": 0.943359375}, {"start": 2049.65, "end": 2049.81, "word": " of", "probability": 0.9521484375}, {"start": 2049.81, "end": 2049.93, "word": " the", "probability": 0.912109375}, {"start": 2049.93, "end": 2050.33, "word": " customer.", "probability": 0.763671875}, {"start": 2050.97, "end": 2050.97, "word": " And", "probability": 0.82568359375}, {"start": 2050.97, "end": 2051.19, "word": " the", "probability": 0.47509765625}, {"start": 2051.19, "end": 2051.65, "word": " third,", "probability": 0.88916015625}, {"start": 2051.81, "end": 2051.93, "word": " we", "probability": 0.71923828125}, {"start": 2051.93, "end": 2052.33, "word": " will", "probability": 0.84033203125}, {"start": 2052.33, "end": 2054.61, "word": " call", "probability": 0.427978515625}, {"start": 2054.61, "end": 2055.91, "word": " from", "probability": 0.833984375}, {"start": 2055.91, "end": 2056.59, "word": " three", "probability": 0.76025390625}, {"start": 2056.59, "end": 2056.79, "word": " to", "probability": 0.9658203125}, {"start": 2056.79, "end": 2057.15, "word": " four", "probability": 0.939453125}, {"start": 2057.15, "end": 2057.59, "word": " calling.", "probability": 0.36376953125}], "temperature": 1.0}, {"id": 85, "seek": 207619, "start": 2059.15, "end": 2076.19, "text": " we arrive to the peak of the response of the customer. So the sales will increase and the profit will increase, but after the time we will face that customer, we arrive to flatter response of the customer.", "tokens": [321, 8881, 281, 264, 10651, 295, 264, 4134, 295, 264, 5474, 13, 407, 264, 5763, 486, 3488, 293, 264, 7475, 486, 3488, 11, 457, 934, 264, 565, 321, 486, 1851, 300, 5474, 11, 321, 8881, 281, 41247, 4134, 295, 264, 5474, 13], "avg_logprob": -0.2714389659637629, "compression_ratio": 1.7457627118644068, "no_speech_prob": 1.6093254089355469e-06, "words": [{"start": 2059.15, "end": 2059.53, "word": " we", "probability": 0.43310546875}, {"start": 2059.53, "end": 2059.91, "word": " arrive", "probability": 0.498291015625}, {"start": 2059.91, "end": 2060.07, "word": " to", "probability": 0.900390625}, {"start": 2060.07, "end": 2060.21, "word": " the", "probability": 0.90234375}, {"start": 2060.21, "end": 2060.41, "word": " peak", "probability": 0.947265625}, {"start": 2060.41, "end": 2060.67, "word": " of", "probability": 0.96142578125}, {"start": 2060.67, "end": 2060.85, "word": " the", "probability": 0.86572265625}, {"start": 2060.85, "end": 2061.23, "word": " response", "probability": 0.9013671875}, {"start": 2061.23, "end": 2061.43, "word": " of", "probability": 0.9580078125}, {"start": 2061.43, "end": 2061.55, "word": " the", "probability": 0.912109375}, {"start": 2061.55, "end": 2063.45, "word": " customer.", "probability": 0.6689453125}, {"start": 2063.77, "end": 2063.77, "word": " So", "probability": 0.560546875}, {"start": 2063.77, "end": 2064.07, "word": " the", "probability": 0.765625}, {"start": 2064.07, "end": 2066.21, "word": " sales", "probability": 0.8115234375}, {"start": 2066.21, "end": 2067.07, "word": " will", "probability": 0.82177734375}, {"start": 2067.07, "end": 2067.49, "word": " increase", "probability": 0.86181640625}, {"start": 2067.49, "end": 2067.71, "word": " and", "probability": 0.84326171875}, {"start": 2067.71, "end": 2067.87, "word": " the", "probability": 0.8720703125}, {"start": 2067.87, "end": 2068.09, "word": " profit", "probability": 0.828125}, {"start": 2068.09, "end": 2068.29, "word": " will", "probability": 0.8779296875}, {"start": 2068.29, "end": 2068.79, "word": " increase,", "probability": 0.8603515625}, {"start": 2069.27, "end": 2069.33, "word": " but", "probability": 0.92138671875}, {"start": 2069.33, "end": 2070.71, "word": " after", "probability": 0.8076171875}, {"start": 2070.71, "end": 2070.99, "word": " the", "probability": 0.79248046875}, {"start": 2070.99, "end": 2071.31, "word": " time", "probability": 0.8974609375}, {"start": 2071.31, "end": 2071.53, "word": " we", "probability": 0.625}, {"start": 2071.53, "end": 2071.73, "word": " will", "probability": 0.82861328125}, {"start": 2071.73, "end": 2072.01, "word": " face", "probability": 0.85498046875}, {"start": 2072.01, "end": 2072.25, "word": " that", "probability": 0.83154296875}, {"start": 2072.25, "end": 2072.67, "word": " customer,", "probability": 0.71044921875}, {"start": 2073.33, "end": 2073.43, "word": " we", "probability": 0.88232421875}, {"start": 2073.43, "end": 2074.09, "word": " arrive", "probability": 0.63916015625}, {"start": 2074.09, "end": 2074.31, "word": " to", "probability": 0.9638671875}, {"start": 2074.31, "end": 2074.75, "word": " flatter", "probability": 0.82373046875}, {"start": 2074.75, "end": 2075.49, "word": " response", "probability": 0.6875}, {"start": 2075.49, "end": 2075.73, "word": " of", "probability": 0.95947265625}, {"start": 2075.73, "end": 2075.85, "word": " the", "probability": 0.90283203125}, {"start": 2075.85, "end": 2076.19, "word": " customer.", "probability": 0.75634765625}], "temperature": 1.0}, {"id": 86, "seek": 209504, "start": 2076.96, "end": 2095.04, "text": " Because he poured of the or shift to the another brand or another company he face may offer in the market. So we will don't respond to the thesis company and we will and it may be decreased or it's decreased.", "tokens": [1436, 415, 23270, 295, 264, 420, 5513, 281, 264, 1071, 3360, 420, 1071, 2237, 415, 1851, 815, 2626, 294, 264, 2142, 13, 407, 321, 486, 500, 380, 4196, 281, 264, 22288, 2237, 293, 321, 486, 293, 309, 815, 312, 24436, 420, 309, 311, 24436, 13], "avg_logprob": -0.29059102029904077, "compression_ratio": 1.548148148148148, "no_speech_prob": 2.086162567138672e-06, "words": [{"start": 2076.96, "end": 2077.48, "word": " Because", "probability": 0.499267578125}, {"start": 2077.48, "end": 2077.74, "word": " he", "probability": 0.83837890625}, {"start": 2077.74, "end": 2078.18, "word": " poured", "probability": 0.385009765625}, {"start": 2078.18, "end": 2078.4, "word": " of", "probability": 0.7373046875}, {"start": 2078.4, "end": 2078.62, "word": " the", "probability": 0.64453125}, {"start": 2078.62, "end": 2079.86, "word": " or", "probability": 0.230224609375}, {"start": 2079.86, "end": 2081.08, "word": " shift", "probability": 0.82763671875}, {"start": 2081.08, "end": 2081.34, "word": " to", "probability": 0.9677734375}, {"start": 2081.34, "end": 2081.44, "word": " the", "probability": 0.505859375}, {"start": 2081.44, "end": 2081.8, "word": " another", "probability": 0.9150390625}, {"start": 2081.8, "end": 2082.56, "word": " brand", "probability": 0.841796875}, {"start": 2082.56, "end": 2082.78, "word": " or", "probability": 0.91845703125}, {"start": 2082.78, "end": 2083.12, "word": " another", "probability": 0.90478515625}, {"start": 2083.12, "end": 2083.62, "word": " company", "probability": 0.927734375}, {"start": 2083.62, "end": 2084.04, "word": " he", "probability": 0.46337890625}, {"start": 2084.04, "end": 2084.28, "word": " face", "probability": 0.2305908203125}, {"start": 2084.28, "end": 2084.62, "word": " may", "probability": 0.61669921875}, {"start": 2084.62, "end": 2085.08, "word": " offer", "probability": 0.93798828125}, {"start": 2085.08, "end": 2085.34, "word": " in", "probability": 0.9267578125}, {"start": 2085.34, "end": 2085.48, "word": " the", "probability": 0.9189453125}, {"start": 2085.48, "end": 2085.84, "word": " market.", "probability": 0.8974609375}, {"start": 2086.2, "end": 2086.36, "word": " So", "probability": 0.93701171875}, {"start": 2086.36, "end": 2086.6, "word": " we", "probability": 0.78955078125}, {"start": 2086.6, "end": 2086.94, "word": " will", "probability": 0.87548828125}, {"start": 2086.94, "end": 2087.92, "word": " don't", "probability": 0.8994140625}, {"start": 2087.92, "end": 2088.32, "word": " respond", "probability": 0.8427734375}, {"start": 2088.32, "end": 2088.56, "word": " to", "probability": 0.966796875}, {"start": 2088.56, "end": 2088.86, "word": " the", "probability": 0.90185546875}, {"start": 2088.86, "end": 2089.22, "word": " thesis", "probability": 0.70654296875}, {"start": 2089.22, "end": 2089.72, "word": " company", "probability": 0.916015625}, {"start": 2089.72, "end": 2090.46, "word": " and", "probability": 0.76220703125}, {"start": 2090.46, "end": 2090.62, "word": " we", "probability": 0.7548828125}, {"start": 2090.62, "end": 2090.94, "word": " will", "probability": 0.88623046875}, {"start": 2090.94, "end": 2091.52, "word": " and", "probability": 0.52294921875}, {"start": 2091.52, "end": 2092.64, "word": " it", "probability": 0.9384765625}, {"start": 2092.64, "end": 2092.98, "word": " may", "probability": 0.8642578125}, {"start": 2092.98, "end": 2093.18, "word": " be", "probability": 0.88720703125}, {"start": 2093.18, "end": 2093.72, "word": " decreased", "probability": 0.78125}, {"start": 2093.72, "end": 2094.02, "word": " or", "probability": 0.90625}, {"start": 2094.02, "end": 2094.5, "word": " it's", "probability": 0.84814453125}, {"start": 2094.5, "end": 2095.04, "word": " decreased.", "probability": 0.931640625}], "temperature": 1.0}, {"id": 87, "seek": 211542, "start": 2095.72, "end": 2115.42, "text": " after the period of time. In these cases we must, we should found another customer or attracting a new customer and make the process at the first one calling and two calling to generate more response of the customer and more profit.", "tokens": [934, 264, 2896, 295, 565, 13, 682, 613, 3331, 321, 1633, 11, 321, 820, 1352, 1071, 5474, 420, 36594, 257, 777, 5474, 293, 652, 264, 1399, 412, 264, 700, 472, 5141, 293, 732, 5141, 281, 8460, 544, 4134, 295, 264, 5474, 293, 544, 7475, 13], "avg_logprob": -0.2588315081337224, "compression_ratio": 1.6180555555555556, "no_speech_prob": 7.748603820800781e-07, "words": [{"start": 2095.72, "end": 2096.06, "word": " after", "probability": 0.232177734375}, {"start": 2096.06, "end": 2096.24, "word": " the", "probability": 0.8125}, {"start": 2096.24, "end": 2096.44, "word": " period", "probability": 0.9697265625}, {"start": 2096.44, "end": 2096.6, "word": " of", "probability": 0.96826171875}, {"start": 2096.6, "end": 2096.86, "word": " time.", "probability": 0.892578125}, {"start": 2097.18, "end": 2097.28, "word": " In", "probability": 0.923828125}, {"start": 2097.28, "end": 2097.46, "word": " these", "probability": 0.74462890625}, {"start": 2097.46, "end": 2097.8, "word": " cases", "probability": 0.921875}, {"start": 2097.8, "end": 2098.06, "word": " we", "probability": 0.50146484375}, {"start": 2098.06, "end": 2098.44, "word": " must,", "probability": 0.59765625}, {"start": 2099.02, "end": 2099.92, "word": " we", "probability": 0.88427734375}, {"start": 2099.92, "end": 2100.14, "word": " should", "probability": 0.95751953125}, {"start": 2100.14, "end": 2100.44, "word": " found", "probability": 0.467041015625}, {"start": 2100.44, "end": 2103.24, "word": " another", "probability": 0.71923828125}, {"start": 2103.24, "end": 2103.74, "word": " customer", "probability": 0.798828125}, {"start": 2103.74, "end": 2104.06, "word": " or", "probability": 0.87548828125}, {"start": 2104.06, "end": 2104.5, "word": " attracting", "probability": 0.79541015625}, {"start": 2104.5, "end": 2104.7, "word": " a", "probability": 0.8779296875}, {"start": 2104.7, "end": 2104.82, "word": " new", "probability": 0.90478515625}, {"start": 2104.82, "end": 2105.22, "word": " customer", "probability": 0.77294921875}, {"start": 2105.22, "end": 2105.72, "word": " and", "probability": 0.8681640625}, {"start": 2105.72, "end": 2106.02, "word": " make", "probability": 0.9306640625}, {"start": 2106.02, "end": 2106.44, "word": " the", "probability": 0.9208984375}, {"start": 2106.44, "end": 2107.32, "word": " process", "probability": 0.884765625}, {"start": 2107.32, "end": 2107.66, "word": " at", "probability": 0.38037109375}, {"start": 2107.66, "end": 2107.98, "word": " the", "probability": 0.68359375}, {"start": 2107.98, "end": 2108.24, "word": " first", "probability": 0.884765625}, {"start": 2108.24, "end": 2108.5, "word": " one", "probability": 0.71630859375}, {"start": 2108.5, "end": 2108.88, "word": " calling", "probability": 0.8310546875}, {"start": 2108.88, "end": 2109.1, "word": " and", "probability": 0.9072265625}, {"start": 2109.1, "end": 2109.24, "word": " two", "probability": 0.90576171875}, {"start": 2109.24, "end": 2109.58, "word": " calling", "probability": 0.8349609375}, {"start": 2109.58, "end": 2110.12, "word": " to", "probability": 0.943359375}, {"start": 2110.12, "end": 2112.32, "word": " generate", "probability": 0.73193359375}, {"start": 2112.32, "end": 2112.88, "word": " more", "probability": 0.9365234375}, {"start": 2112.88, "end": 2113.78, "word": " response", "probability": 0.9375}, {"start": 2113.78, "end": 2113.94, "word": " of", "probability": 0.9365234375}, {"start": 2113.94, "end": 2114.04, "word": " the", "probability": 0.90869140625}, {"start": 2114.04, "end": 2114.38, "word": " customer", "probability": 0.75634765625}, {"start": 2114.38, "end": 2114.88, "word": " and", "probability": 0.921875}, {"start": 2114.88, "end": 2115.08, "word": " more", "probability": 0.9365234375}, {"start": 2115.08, "end": 2115.42, "word": " profit.", "probability": 0.9052734375}], "temperature": 1.0}, {"id": 88, "seek": 214773, "start": 2120.93, "end": 2147.73, "text": " Let's try to return back to the shortcomings or limitations of the portfolio model. In portfolio model, imagine we are going to talk about two customers here or three or four. Let's ask ourselves this simple question. Do you think is it enough to distinguish among these three customers according to their level of attractiveness? It isn't enough.", "tokens": [961, 311, 853, 281, 2736, 646, 281, 264, 2099, 49886, 420, 15705, 295, 264, 12583, 2316, 13, 682, 12583, 2316, 11, 3811, 321, 366, 516, 281, 751, 466, 732, 4581, 510, 420, 1045, 420, 1451, 13, 961, 311, 1029, 4175, 341, 2199, 1168, 13, 1144, 291, 519, 307, 309, 1547, 281, 20206, 3654, 613, 1045, 4581, 4650, 281, 641, 1496, 295, 5049, 8477, 30, 467, 1943, 380, 1547, 13], "avg_logprob": -0.1517857074737549, "compression_ratio": 1.6036866359447004, "no_speech_prob": 0.0, "words": [{"start": 2120.93, "end": 2121.17, "word": " Let's", "probability": 0.786376953125}, {"start": 2121.17, "end": 2121.35, "word": " try", "probability": 0.429931640625}, {"start": 2121.35, "end": 2121.51, "word": " to", "probability": 0.95068359375}, {"start": 2121.51, "end": 2121.75, "word": " return", "probability": 0.86767578125}, {"start": 2121.75, "end": 2122.09, "word": " back", "probability": 0.74755859375}, {"start": 2122.09, "end": 2122.31, "word": " to", "probability": 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"probability": 0.88623046875}, {"start": 2128.93, "end": 2129.11, "word": " we", "probability": 0.9052734375}, {"start": 2129.11, "end": 2129.23, "word": " are", "probability": 0.85009765625}, {"start": 2129.23, "end": 2129.41, "word": " going", "probability": 0.9296875}, {"start": 2129.41, "end": 2129.57, "word": " to", "probability": 0.9736328125}, {"start": 2129.57, "end": 2129.77, "word": " talk", "probability": 0.8974609375}, {"start": 2129.77, "end": 2130.05, "word": " about", "probability": 0.89990234375}, {"start": 2130.05, "end": 2130.27, "word": " two", "probability": 0.77197265625}, {"start": 2130.27, "end": 2130.79, "word": " customers", "probability": 0.84521484375}, {"start": 2130.79, "end": 2131.05, "word": " here", "probability": 0.8232421875}, {"start": 2131.05, "end": 2132.09, "word": " or", "probability": 0.47705078125}, {"start": 2132.09, "end": 2132.45, "word": " three", "probability": 0.91748046875}, {"start": 2132.45, "end": 2132.69, "word": " or", "probability": 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0.9716796875}, {"start": 2145.53, "end": 2146.15, "word": " attractiveness?", "probability": 0.89990234375}, {"start": 2146.57, "end": 2147.19, "word": " It", "probability": 0.86181640625}, {"start": 2147.19, "end": 2147.43, "word": " isn't", "probability": 0.92431640625}, {"start": 2147.43, "end": 2147.73, "word": " enough.", "probability": 0.86865234375}], "temperature": 1.0}, {"id": 89, "seek": 217555, "start": 2148.93, "end": 2175.55, "text": " Why? Because we are talking about a customer. This customer is a person, a human being. He is influenced by various or by more than one factor, by more than one characteristics, by more than one issue. He is a human being, he is thinking, he is having feelings, he is having emotions and so on. Because of this, we cannot classify persons according to these four quadrants.", "tokens": [1545, 30, 1436, 321, 366, 1417, 466, 257, 5474, 13, 639, 5474, 307, 257, 954, 11, 257, 1952, 885, 13, 634, 307, 15269, 538, 3683, 420, 538, 544, 813, 472, 5952, 11, 538, 544, 813, 472, 10891, 11, 538, 544, 813, 472, 2734, 13, 634, 307, 257, 1952, 885, 11, 415, 307, 1953, 11, 415, 307, 1419, 6640, 11, 415, 307, 1419, 8462, 293, 370, 322, 13, 1436, 295, 341, 11, 321, 2644, 33872, 14453, 4650, 281, 613, 1451, 10787, 10968, 13], "avg_logprob": -0.13676581899803805, "compression_ratio": 1.7725118483412323, "no_speech_prob": 0.0, "words": [{"start": 2148.93, "end": 2149.41, "word": " Why?", "probability": 0.6962890625}, {"start": 2149.85, "end": 2150.15, "word": " Because", "probability": 0.857421875}, {"start": 2150.15, "end": 2150.29, "word": " we", "probability": 0.9228515625}, {"start": 2150.29, "end": 2150.39, "word": " are", "probability": 0.880859375}, {"start": 2150.39, "end": 2150.67, "word": 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But what about the other factors of this person? Are these discussed by portfolio model? No, they are not discussed. A second thing, process doesn't arrive at the optimal location of the sales call. What does it mean optimal?", "tokens": [682, 1184, 46856, 11, 321, 2644, 33872, 264, 954, 4650, 281, 472, 5952, 11, 570, 510, 321, 366, 1417, 466, 472, 5952, 13, 583, 437, 466, 264, 661, 6771, 295, 341, 954, 30, 2014, 613, 7152, 538, 12583, 2316, 30, 883, 11, 436, 366, 406, 7152, 13, 316, 1150, 551, 11, 1399, 1177, 380, 8881, 412, 264, 16252, 4914, 295, 264, 5763, 818, 13, 708, 775, 309, 914, 16252, 30], "avg_logprob": -0.21170774396036712, "compression_ratio": 1.5825688073394495, "no_speech_prob": 0.0, "words": [{"start": 2177.13, "end": 2177.39, "word": " In", "probability": 0.79345703125}, {"start": 2177.39, "end": 2177.69, "word": " each", "probability": 0.93603515625}, {"start": 2177.69, "end": 2178.15, "word": " quadrant,", "probability": 0.97216796875}, {"start": 2178.67, "end": 2178.85, "word": " we", "probability": 0.93701171875}, {"start": 2178.85, "end": 2179.23, "word": " cannot", "probability": 0.779296875}, {"start": 2179.23, "end": 2179.77, "word": " classify", 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How we should talk about the sales calls? In other words, how many phone calls should we give for each customer out of the four types of our prospects? Has the portfolio model provided us with any information about these things? Of course not. Then we began talking about the decision model. The decision model tried to cover this gap.", "tokens": [7587, 11, 264, 7157, 27599, 295, 264, 5763, 818, 13, 1012, 321, 820, 751, 466, 264, 5763, 5498, 30, 682, 661, 2283, 11, 577, 867, 2593, 5498, 820, 321, 976, 337, 1184, 5474, 484, 295, 264, 1451, 3467, 295, 527, 32933, 30, 8646, 264, 12583, 2316, 5649, 505, 365, 604, 1589, 466, 613, 721, 30, 2720, 1164, 406, 13, 1396, 321, 4283, 1417, 466, 264, 3537, 2316, 13, 440, 3537, 2316, 3031, 281, 2060, 341, 7417, 13], "avg_logprob": -0.1791867012014756, "compression_ratio": 1.673913043478261, "no_speech_prob": 0.0, "words": [{"start": 2201.4100000000003, "end": 2202.01, "word": " Exactly,", "probability": 0.681640625}, {"start": 2202.15, "end": 2202.25, "word": " the", "probability": 0.8330078125}, {"start": 2202.25, "end": 2202.59, "word": " ideal", "probability": 0.67236328125}, {"start": 2202.59, "end": 2203.29, "word": " allocation", "probability": 0.86279296875}, {"start": 2203.29, "end": 2203.59, "word": " of", "probability": 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So do you think is there any need to make further phone calls? No. Number two, this customer might be saturated by our product or service. 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