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If you are able to do that, this means your sales team is efficient. If not, this means our sales team is not efficient. The third thing which is effectiveness. It means a buyer's favorable reaction to the sales effort. 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Any questions or comments? Go on. Fundamental structures for organization sales force are four. This is very essential, very crucial.", "tokens": [17764, 8477, 1355, 264, 3485, 281, 9021, 527, 992, 5493, 420, 15961, 13, 2639, 1651, 420, 3053, 30, 1037, 322, 13, 13493, 44538, 9227, 337, 4475, 5763, 3464, 366, 1451, 13, 639, 307, 588, 7115, 11, 588, 11462, 13], "avg_logprob": -0.27343748733401296, "compression_ratio": 1.375, "no_speech_prob": 5.960464477539063e-08, "words": [{"start": 546.92, "end": 547.64, "word": " Effectiveness", "probability": 0.7186279296875}, {"start": 547.64, "end": 547.92, "word": " means", "probability": 0.7861328125}, {"start": 547.92, "end": 548.12, "word": " the", "probability": 0.76513671875}, {"start": 548.12, "end": 548.4, "word": " ability", "probability": 0.94189453125}, {"start": 548.4, "end": 548.6, "word": " to", "probability": 0.96337890625}, {"start": 548.6, "end": 549.02, "word": " accomplish", "probability": 0.8681640625}, {"start": 549.02, "end": 549.32, "word": " our", "probability": 0.8076171875}, {"start": 549.32, "end": 549.64, "word": " set", "probability": 0.9111328125}, {"start": 549.64, "end": 550.0, "word": " goals", "probability": 0.9189453125}, {"start": 550.0, "end": 550.16, "word": " or", "probability": 0.8134765625}, {"start": 550.16, "end": 550.66, "word": " objectives.", "probability": 0.84521484375}, {"start": 551.64, "end": 551.9, "word": " Any", "probability": 0.708984375}, {"start": 551.9, "end": 552.28, "word": " questions", "probability": 0.5869140625}, {"start": 552.28, "end": 552.44, "word": " or", "probability": 0.55322265625}, {"start": 552.44, "end": 552.84, "word": " comments?", "probability": 0.86865234375}, {"start": 553.48, "end": 553.72, "word": " Go", "probability": 0.76904296875}, {"start": 553.72, "end": 553.92, "word": " on.", "probability": 0.93603515625}, {"start": 554.74, "end": 555.46, "word": " Fundamental", "probability": 0.829345703125}, {"start": 555.46, "end": 556.16, "word": " structures", "probability": 0.8291015625}, {"start": 556.16, "end": 556.38, "word": " for", "probability": 0.86279296875}, {"start": 556.38, "end": 556.78, "word": " organization", "probability": 0.372802734375}, {"start": 556.78, "end": 557.16, "word": " sales", "probability": 0.64599609375}, {"start": 557.16, "end": 557.52, "word": " force", "probability": 0.6552734375}, {"start": 557.52, "end": 557.84, "word": " are", "probability": 0.8916015625}, {"start": 557.84, "end": 558.3, "word": " four.", "probability": 0.396240234375}, {"start": 558.74, "end": 558.96, "word": " This", "probability": 0.7626953125}, {"start": 558.96, "end": 559.16, "word": " is", "probability": 0.94677734375}, {"start": 559.16, "end": 559.4, "word": " very", "probability": 0.78759765625}, {"start": 559.4, "end": 559.86, "word": " essential,", "probability": 0.78662109375}, {"start": 560.4, "end": 560.62, "word": " very", "probability": 0.83203125}, {"start": 560.62, "end": 560.98, "word": " crucial.", "probability": 0.90087890625}], "temperature": 1.0}, {"id": 23, "seek": 57741, "start": 561.92, "end": 577.42, "text": " We will talk about the generalist. Listen, this model was very popular and applicable before 1950. 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So they began innovating three kinds of models by which we are going to organize our staff members. So we'll talk about product specialization, third customer specialization, and the final one, the functional specialized model. All these things, they are models for organizational structures for sales staff members. 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The generalist structure, this model is considered to be the most feasible and comfortable and convenient model according to which we can organize our sales staff members or sales force. Somebody is going to wonder, this model is determined by what? 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In other words, if I am going to help Wala and Hanin and Alia and Zakia and how you are going to hire this says staff member, I am going to distribute them according to geography. 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Let's give example. The generalist model, first, what did we say? It is going to achieve a best and excellent result if it is going to refer to something called consumed products. Consumed products mainly the food item, the beverage item, and the clothes.", "tokens": [1133, 321, 366, 1566, 42632, 11, 321, 366, 13761, 281, 264, 1349, 2531, 13, 961, 311, 976, 1365, 13, 440, 2674, 468, 2316, 11, 700, 11, 437, 630, 321, 584, 30, 467, 307, 516, 281, 4584, 257, 1151, 293, 7103, 1874, 498, 309, 307, 516, 281, 2864, 281, 746, 1219, 21226, 3383, 13, 6923, 28189, 3383, 8704, 264, 1755, 3174, 11, 264, 35519, 3174, 11, 293, 264, 5534, 13], "avg_logprob": -0.22924107185431888, "compression_ratio": 1.5902439024390245, "no_speech_prob": 0.0, "words": [{"start": 720.68, "end": 720.9, "word": " When", "probability": 0.76904296875}, {"start": 720.9, "end": 721.02, "word": " we", "probability": 0.94873046875}, {"start": 721.02, "end": 721.14, "word": " are", "probability": 0.7353515625}, {"start": 721.14, "end": 721.58, "word": " saying", "probability": 0.890625}, {"start": 721.58, "end": 722.04, "word": " homogeneous,", "probability": 0.759765625}, {"start": 722.26, "end": 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Because these items of products, they are homogeneous. Homogeneous for what? For every person who are living on the face of the earth. This is one thing. Later on, the third person who is going to work according to the generalist structure, he is going to understand something called the culture. 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When we are saying culture, we are saying habits, traditions, what the people like, what the people do not like. Stop here. Somebody is going to say culture is affecting these things when we are talking about sales. Yes. 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Somebody is going to say why? Because this isn't related to our culture. This isn't related to what we used to eat and consume. This is a fact, it's a fact. Did it happen with you too much? How do you spend your life? 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This is considered to be one of the big disadvantages. Now, when so many products to sell, it is difficult to have expertise needed to develop a creative solution to customer problems. Stop here. Another disadvantage for this model is the following.", "tokens": [3082, 552, 544, 4630, 11, 544, 565, 281, 1223, 439, 264, 4581, 7101, 436, 366, 516, 281, 2028, 365, 13, 639, 307, 4888, 281, 312, 472, 295, 264, 955, 37431, 13, 823, 11, 562, 370, 867, 3383, 281, 3607, 11, 309, 307, 2252, 281, 362, 11769, 2978, 281, 1499, 257, 5880, 3827, 281, 5474, 2740, 13, 5535, 510, 13, 3996, 24292, 337, 341, 2316, 307, 264, 3480, 13], "avg_logprob": -0.20210597826086957, "compression_ratio": 1.5555555555555556, "no_speech_prob": 0.0, "words": [{"start": 978.11, "end": 978.53, "word": " cause", "probability": 0.2744140625}, {"start": 978.53, "end": 978.85, "word": " them", "probability": 0.88134765625}, {"start": 978.85, "end": 979.65, "word": " more", "probability": 0.8857421875}, {"start": 979.65, "end": 980.07, "word": " effort,", "probability": 0.7978515625}, {"start": 980.45, "end": 980.65, "word": " more", "probability": 0.9052734375}, {"start": 980.65, "end": 981.05, "word": " time", 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Yes, of course yes.", "tokens": [759, 257, 23523, 7262, 420, 257, 23523, 587, 1361, 281, 291, 382, 257, 954, 567, 307, 1364, 4650, 281, 264, 17277, 2316, 11, 293, 415, 576, 411, 337, 1365, 281, 2256, 512, 295, 264, 3174, 295, 264, 1755, 11, 281, 5940, 257, 6349, 420, 257, 44185, 420, 2035, 11, 307, 341, 516, 281, 312, 257, 1154, 337, 257, 5763, 10813, 567, 307, 1364, 4650, 281, 264, 17277, 3877, 30, 1079, 11, 295, 1164, 2086, 13], "avg_logprob": -0.193153782502601, "compression_ratio": 1.7272727272727273, "no_speech_prob": 1.1920928955078125e-07, "words": [{"start": 1001.95, "end": 1002.41, "word": " If", "probability": 0.81787109375}, {"start": 1002.41, "end": 1002.75, "word": " a", "probability": 0.97509765625}, {"start": 1002.75, "end": 1003.17, "word": " Swedish", "probability": 0.83203125}, {"start": 1003.17, "end": 1003.61, "word": " lady", "probability": 0.82275390625}, {"start": 1003.61, "end": 1003.89, "word": " or", "probability": 0.9287109375}, {"start": 1003.89, "end": 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The answer is, this person is Swedish, and if he is Swedish, he doesn't belong to the homogeneous group which I know. So we are going to have what? A problem. Can I give him a solution? I cannot. Are you following me?", "tokens": [13463, 307, 516, 281, 584, 11, 983, 30, 440, 1867, 307, 11, 341, 954, 307, 23523, 11, 293, 498, 415, 307, 23523, 11, 415, 1177, 380, 5784, 281, 264, 42632, 1594, 597, 286, 458, 13, 407, 321, 366, 516, 281, 362, 437, 30, 316, 1154, 13, 1664, 286, 976, 796, 257, 3827, 30, 286, 2644, 13, 2014, 291, 3480, 385, 30], "avg_logprob": -0.1848538356923288, "compression_ratio": 1.439306358381503, "no_speech_prob": 5.960464477539063e-08, "words": [{"start": 1026.98, "end": 1027.46, "word": " Somebody", "probability": 0.5673828125}, {"start": 1027.46, "end": 1027.68, "word": " is", "probability": 0.63330078125}, {"start": 1027.68, "end": 1027.82, "word": " going", "probability": 0.9345703125}, {"start": 1027.82, "end": 1027.98, "word": " to", "probability": 0.97265625}, {"start": 1027.98, "end": 1028.28, "word": " say,", "probability": 0.89599609375}, {"start": 1028.96, "end": 1029.48, "word": " why?", "probability": 0.50927734375}, {"start": 1030.04, "end": 1030.3, "word": " The", "probability": 0.82958984375}, {"start": 1030.3, "end": 1030.62, "word": " answer", "probability": 0.9609375}, {"start": 1030.62, "end": 1031.06, "word": " is,", "probability": 0.95068359375}, {"start": 1031.86, "end": 1032.14, "word": " this", "probability": 0.890625}, {"start": 1032.14, "end": 1032.52, "word": " person", "probability": 0.92041015625}, {"start": 1032.52, "end": 1032.74, "word": " is", "probability": 0.94970703125}, {"start": 1032.74, "end": 1033.24, "word": " Swedish,", "probability": 0.75341796875}, {"start": 1033.74, "end": 1033.84, "word": " and", "probability": 0.6435546875}, {"start": 1033.84, "end": 1034.0, "word": " if", "probability": 0.94873046875}, {"start": 1034.0, "end": 1034.12, "word": " he", "probability": 0.9609375}, {"start": 1034.12, "end": 1034.3, "word": " is", "probability": 0.8701171875}, {"start": 1034.3, "end": 1034.8, "word": " Swedish,", "probability": 0.89013671875}, {"start": 1035.18, "end": 1035.34, "word": " he", "probability": 0.9130859375}, {"start": 1035.34, "end": 1035.72, "word": " doesn't", "probability": 0.846435546875}, {"start": 1035.72, "end": 1035.92, "word": " belong", "probability": 0.865234375}, {"start": 1035.92, "end": 1036.12, "word": " to", "probability": 0.95751953125}, {"start": 1036.12, "end": 1036.26, "word": " the", "probability": 0.826171875}, {"start": 1036.26, "end": 1036.62, "word": " homogeneous", "probability": 0.6142578125}, {"start": 1036.62, "end": 1037.12, "word": " group", "probability": 0.96240234375}, {"start": 1037.12, "end": 1037.62, "word": " which", "probability": 0.4814453125}, {"start": 1037.62, "end": 1037.84, "word": " I", "probability": 0.98583984375}, {"start": 1037.84, "end": 1038.12, "word": " know.", "probability": 0.88232421875}, {"start": 1039.42, "end": 1039.68, "word": " So", "probability": 0.93017578125}, {"start": 1039.68, "end": 1039.84, "word": " we", "probability": 0.61669921875}, {"start": 1039.84, "end": 1039.96, "word": " are", "probability": 0.89697265625}, {"start": 1039.96, "end": 1040.2, "word": " going", "probability": 0.94482421875}, {"start": 1040.2, "end": 1040.36, "word": " to", "probability": 0.96923828125}, {"start": 1040.36, "end": 1040.54, "word": " have", "probability": 0.94775390625}, {"start": 1040.54, "end": 1040.88, "word": " what?", "probability": 0.82373046875}, {"start": 1041.0, "end": 1041.18, "word": " A", "probability": 0.951171875}, {"start": 1041.18, "end": 1041.6, "word": " problem.", "probability": 0.86767578125}, {"start": 1041.96, "end": 1042.18, "word": " Can", "probability": 0.958984375}, {"start": 1042.18, "end": 1042.34, "word": " I", "probability": 0.99267578125}, {"start": 1042.34, "end": 1042.58, "word": " give", "probability": 0.89306640625}, {"start": 1042.58, "end": 1042.86, "word": " him", "probability": 0.91845703125}, {"start": 1042.86, "end": 1043.2, "word": " a", "probability": 0.98681640625}, {"start": 1043.2, "end": 1043.6, "word": " solution?", "probability": 0.9521484375}, {"start": 1043.76, "end": 1043.92, "word": " I", "probability": 0.9755859375}, {"start": 1043.92, "end": 1044.2, "word": " cannot.", "probability": 0.73681640625}, {"start": 1046.42, "end": 1046.94, "word": " Are", "probability": 0.92822265625}, {"start": 1046.94, "end": 1047.04, "word": " you", "probability": 0.9619140625}, {"start": 1047.04, "end": 1047.22, "word": " following", "probability": 0.931640625}, {"start": 1047.22, "end": 1047.44, "word": " me?", "probability": 0.95556640625}], "temperature": 1.0}, {"id": 42, "seek": 107079, "start": 1048.61, "end": 1070.79, "text": " So, all the time remember, the sales persons who are working according to the general structure, they will do their best if they are dealing with customers who are belonging to the same homogeneous group of the customer. But if one of them is approaching from outside this group, we will encounter difficulty with him or her. Clear?", "tokens": [407, 11, 439, 264, 565, 1604, 11, 264, 5763, 14453, 567, 366, 1364, 4650, 281, 264, 2674, 3877, 11, 436, 486, 360, 641, 1151, 498, 436, 366, 6260, 365, 4581, 567, 366, 22957, 281, 264, 912, 42632, 1594, 295, 264, 5474, 13, 583, 498, 472, 295, 552, 307, 14908, 490, 2380, 341, 1594, 11, 321, 486, 8593, 10360, 365, 796, 420, 720, 13, 14993, 30], "avg_logprob": -0.16785037336927472, "compression_ratio": 1.6485148514851484, "no_speech_prob": 5.960464477539063e-08, "words": [{"start": 1048.61, "end": 1049.05, "word": " So,", "probability": 0.798828125}, {"start": 1049.39, "end": 1049.65, "word": " all", "probability": 0.85546875}, {"start": 1049.65, "end": 1049.81, "word": " the", "probability": 0.90771484375}, {"start": 1049.81, "end": 1050.03, "word": " time", "probability": 0.88623046875}, {"start": 1050.03, "end": 1050.51, "word": " remember,", "probability": 0.6552734375}, {"start": 1050.97, "end": 1051.09, "word": " the", "probability": 0.7998046875}, {"start": 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So, many organizations have decided that they need to specialize beyond simple geography. This happened when? Especially after 1950. 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The advantages, best for homogeneous group of buyers. Another advantage, in-depth knowledge of the local culture, economic and competitive condition, as we explained. The disadvantages, number one, lack knowledge of the product. Stop here. If you are going to ask Walaa or Fatima,", "tokens": [1407, 20858, 11, 437, 366, 264, 14906, 295, 264, 2674, 468, 2316, 293, 1080, 37431, 30, 440, 14906, 11, 1151, 337, 42632, 1594, 295, 23465, 13, 3996, 5002, 11, 294, 12, 25478, 3601, 295, 264, 2654, 3713, 11, 4836, 293, 10043, 4188, 11, 382, 321, 8825, 13, 440, 37431, 11, 1230, 472, 11, 5011, 3601, 295, 264, 1674, 13, 5535, 510, 13, 759, 291, 366, 516, 281, 1029, 9707, 4332, 420, 16948, 4775, 11], "avg_logprob": -0.26500001112620036, "compression_ratio": 1.6194690265486726, "no_speech_prob": 0.0, "words": [{"start": 1142.84, "end": 1143.04, "word": " To", "probability": 0.79833984375}, {"start": 1143.04, "end": 1143.36, "word": " summarize,", "probability": 0.79736328125}, {"start": 1143.62, "end": 1143.74, "word": " what", "probability": 0.9013671875}, {"start": 1143.74, "end": 1143.88, "word": " are", "probability": 0.93017578125}, {"start": 1143.88, "end": 1144.04, "word": " the", "probability": 0.90234375}, {"start": 1144.04, 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This person or salesperson might not know the answer unless he is going to read the ingredients on the packet of the product. Why? Look at here. The generalist structure, we are talking about expansion horizontally. We know this product and this product and this product and this product and this product and so on. Horizontal knowledge. The horizontal knowledge is going to know what?", "tokens": [639, 1674, 11, 437, 366, 1080, 1065, 6952, 30, 639, 954, 420, 5763, 10813, 1062, 406, 458, 264, 1867, 5969, 415, 307, 516, 281, 1401, 264, 6952, 322, 264, 20300, 295, 264, 1674, 13, 1545, 30, 2053, 412, 510, 13, 440, 2674, 468, 3877, 11, 321, 366, 1417, 466, 11260, 33796, 13, 492, 458, 341, 1674, 293, 341, 1674, 293, 341, 1674, 293, 341, 1674, 293, 341, 1674, 293, 370, 322, 13, 42141, 896, 304, 3601, 13, 440, 12750, 3601, 307, 516, 281, 458, 437, 30], "avg_logprob": -0.1720545987288157, "compression_ratio": 2.0093457943925235, "no_speech_prob": 0.0, "words": [{"start": 1168.1, "end": 1168.48, "word": " This", "probability": 0.5302734375}, {"start": 1168.48, "end": 1168.92, "word": " product,", "probability": 0.873046875}, {"start": 1169.4, "end": 1169.58, "word": " what", "probability": 0.90185546875}, {"start": 1169.58, "end": 1169.78, "word": " are", "probability": 0.921875}, {"start": 1169.78, "end": 1169.96, "word": " 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Somebody is going to say, what's the meaning of vertical knowledge? This one. Everything about this product, can we know that? We cannot. Why? Because this is a natural thing in the mind of human beings. So the expansion or the horizontal expansion of the knowledge is going to be on the account of a specialized vertical knowledge about a product or a thing.", "tokens": [2621, 9429, 3601, 13, 13463, 307, 516, 281, 584, 11, 437, 311, 264, 3620, 295, 9429, 3601, 30, 639, 472, 13, 5471, 466, 341, 1674, 11, 393, 321, 458, 300, 30, 492, 2644, 13, 1545, 30, 1436, 341, 307, 257, 3303, 551, 294, 264, 1575, 295, 1952, 8958, 13, 407, 264, 11260, 420, 264, 12750, 11260, 295, 264, 3601, 307, 516, 281, 312, 322, 264, 2696, 295, 257, 19813, 9429, 3601, 466, 257, 1674, 420, 257, 551, 13], "avg_logprob": -0.20213607217692123, "compression_ratio": 1.7454545454545454, "no_speech_prob": 5.960464477539063e-08, "words": [{"start": 1197.11, "end": 1197.49, "word": " Our", "probability": 0.350830078125}, {"start": 1197.49, "end": 1197.99, "word": " vertical", "probability": 0.76953125}, {"start": 1197.99, "end": 1198.37, "word": " knowledge.", "probability": 0.93017578125}, {"start": 1199.43, "end": 1199.81, 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So in this case, Fatima or Wala might say, give me a minute and he will begin or she will begin reading the ingredients and trying to figure out where this product was manufactured and so on. They do not have deep technical knowledge about 1X product. Why? 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A second disadvantage, inability to create solutions for the customer problems. As we said, if a person doesn't belong to the homogeneous group, we cannot solve the problem for him or her. And a third disadvantage, focus on easy to sell customers. This is the thing which we like. But we are not ready to work with any troublemaker buyers or any troublemaker consumers. 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This is very important and this is an example. Example of what? Example of an organizational structure about how we can organize our sales team according to the generalist model. 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In other words, we are able to produce more than one kind, one nature of a product. Sometimes I'm going to print printer, server, micro computers, programmable calculators, copiers. Copiers means, and the final one, large computers. So how many kinds of products we are talking about here? 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It's very difficult. Why? We are talking about complicated products which require different technical knowledge. So what should we do? We should distribute our sales staff members according to the product. 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The same thing with these things. Exactly. We are talking about the vertical knowledge rather than the horizontal one. Clear? Any questions and comments? Now go on. So let's sum up. What are the advantages of this product specialization model? 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Listen here. Having the same technical knowledge. Excellent. 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What is going to happen? The management will try to convince her to stay. Why? Because we need her too much. If she insisted, then what we are going to do? We are going to replace her. The replacement is expensive. 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In this scenario, the company or the management might tell him or her, we are going to offer you for example 1000 US dollars as a salary. This person might say, Wallah it isn't enough for me. What do you want? 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We do not think so. Why? Because this person is occupying or is having a technical knowledge. And we need this technical knowledge. Because of this what we are saying. We are saying it is expensive. Clear? Second, may result in duplication of sales calls to clients. This is very dangerous. Somebody is going to say, what is the meaning of this? 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Imagine Fatima is working as a specialized salesperson for a printer. Hanin is working as a specialized salesperson for a server. I am a customer. Hanin might call me talking about an offer of server. Wallahi, after less than an hour, Fatima will call the same customer. She will call me talking about a new offer of the printer. 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Very bad. Why very bad? Because we mentioned this last time, we should not be seen as pushy. We should not be seen as intruder. Because of this, we are saying this product specialized model, it requires a huge effort of coordination in order to avoid duplication and in order not to confuse our own customers. Clear?", "tokens": [708, 307, 264, 9995, 597, 264, 5474, 307, 516, 281, 362, 466, 552, 30, 4372, 1578, 13, 1545, 588, 1578, 30, 1436, 321, 2835, 341, 1036, 565, 11, 321, 820, 406, 312, 1612, 382, 2944, 88, 13, 492, 820, 406, 312, 1612, 382, 17467, 28230, 13, 1436, 295, 341, 11, 321, 366, 1566, 341, 1674, 19813, 2316, 11, 309, 7029, 257, 2603, 4630, 295, 21252, 294, 1668, 281, 5042, 17154, 399, 293, 294, 1668, 406, 281, 28584, 527, 1065, 4581, 13, 14993, 30], "avg_logprob": -0.1874999948555515, "compression_ratio": 1.6724137931034482, "no_speech_prob": 0.0, "words": [{"start": 1642.95, "end": 1643.17, "word": " What", "probability": 0.35791015625}, {"start": 1643.17, "end": 1643.39, "word": " is", "probability": 0.8203125}, {"start": 1643.39, "end": 1643.51, "word": " the", "probability": 0.85693359375}, {"start": 1643.51, "end": 1643.83, "word": " impression", "probability": 0.82861328125}, {"start": 1643.83, "end": 1644.19, "word": " which", "probability": 0.61181640625}, {"start": 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Let's conclude by the third one which is customer. Now our priority is not the product but the customer itself or himself or herself. 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We underline the selected types of buyers because the backbone of distributing our sales team member is the type of the customer.", "tokens": [10839, 611, 382, 257, 9429, 6370, 2831, 813, 12750, 6370, 11, 294, 597, 1184, 5763, 10813, 420, 5763, 1469, 5112, 264, 2302, 1674, 1622, 281, 8209, 3467, 295, 23465, 13, 492, 833, 1889, 264, 8209, 3467, 295, 23465, 570, 264, 34889, 295, 41406, 527, 5763, 1469, 4006, 307, 264, 2010, 295, 264, 5474, 13], "avg_logprob": -0.18991477977145801, "compression_ratio": 1.7102272727272727, "no_speech_prob": 0.0, "words": [{"start": 1689.42, "end": 1690.08, "word": " referred", "probability": 0.7109375}, {"start": 1690.08, "end": 1690.48, "word": " also", "probability": 0.767578125}, {"start": 1690.48, "end": 1690.9, "word": " as", "probability": 0.939453125}, {"start": 1690.9, "end": 1691.3, "word": " a", "probability": 0.79248046875}, {"start": 1691.3, "end": 1691.66, "word": " vertical", "probability": 0.72509765625}, {"start": 1691.66, "end": 1692.24, "word": " marketing", "probability": 0.71240234375}, {"start": 1692.24, "end": 1692.68, "word": " rather", "probability": 0.8330078125}, {"start": 1692.68, "end": 1693.24, "word": " than", "probability": 0.94775390625}, {"start": 1693.24, "end": 1694.16, "word": " horizontal", "probability": 0.7490234375}, {"start": 1694.16, "end": 1694.76, "word": " marketing,", "probability": 0.72314453125}, {"start": 1695.18, "end": 1695.34, "word": " in", "probability": 0.93505859375}, {"start": 1695.34, "end": 1695.62, "word": " which", "probability": 0.9453125}, {"start": 1695.62, "end": 1696.02, "word": " each", "probability": 0.94287109375}, {"start": 1696.02, "end": 1696.74, "word": " salesperson", "probability": 0.919677734375}, {"start": 1696.74, "end": 1697.0, "word": " or", "probability": 0.93310546875}, {"start": 1697.0, "end": 1697.32, "word": " sales", "probability": 0.94775390625}, {"start": 1697.32, "end": 1697.6, "word": " team", "probability": 0.93359375}, {"start": 1697.6, "end": 1698.2, "word": " tells", "probability": 0.59375}, {"start": 1698.2, "end": 1698.68, "word": " the", "probability": 0.90771484375}, {"start": 1698.68, "end": 1699.08, "word": " entire", "probability": 0.927734375}, {"start": 1699.08, "end": 1699.38, "word": " product", "probability": 0.87158203125}, {"start": 1699.38, "end": 1699.8, "word": " line", "probability": 0.9052734375}, {"start": 1699.8, "end": 1699.96, "word": " to", "probability": 0.96533203125}, {"start": 1699.96, "end": 1700.34, "word": " selected", "probability": 0.5}, {"start": 1700.34, "end": 1700.62, "word": " types", "probability": 0.483642578125}, {"start": 1700.62, "end": 1700.74, "word": " of", "probability": 0.97021484375}, {"start": 1700.74, "end": 1701.02, "word": " buyers.", "probability": 0.94482421875}, {"start": 1701.72, "end": 1701.92, "word": " We", "probability": 0.8408203125}, {"start": 1701.92, "end": 1702.54, "word": " underline", "probability": 0.930908203125}, {"start": 1702.54, "end": 1702.74, "word": " the", "probability": 0.859375}, {"start": 1702.74, "end": 1703.04, "word": " selected", "probability": 0.8115234375}, {"start": 1703.04, "end": 1703.44, "word": " types", "probability": 0.76953125}, {"start": 1703.44, "end": 1703.58, "word": " of", "probability": 0.966796875}, {"start": 1703.58, "end": 1703.88, "word": " buyers", "probability": 0.93212890625}, {"start": 1703.88, "end": 1705.36, "word": " because", "probability": 0.42724609375}, {"start": 1705.36, "end": 1705.62, "word": " the", "probability": 0.91845703125}, {"start": 1705.62, "end": 1705.94, "word": " backbone", "probability": 0.93896484375}, {"start": 1705.94, "end": 1706.62, "word": " of", "probability": 0.966796875}, {"start": 1706.62, "end": 1707.26, "word": " distributing", "probability": 0.84033203125}, {"start": 1707.26, "end": 1707.58, "word": " our", "probability": 0.87646484375}, {"start": 1707.58, "end": 1707.88, "word": " sales", "probability": 0.9384765625}, {"start": 1707.88, "end": 1708.1, "word": " team", "probability": 0.98046875}, {"start": 1708.1, "end": 1708.36, "word": " member", "probability": 0.446533203125}, {"start": 1708.36, "end": 1708.78, "word": " is", "probability": 0.853515625}, {"start": 1708.78, "end": 1709.94, "word": " the", "probability": 0.83349609375}, {"start": 1709.94, "end": 1710.38, "word": " type", "probability": 0.95751953125}, {"start": 1710.38, "end": 1710.58, "word": " of", "probability": 0.96533203125}, {"start": 1710.58, "end": 1710.72, "word": " the", "probability": 0.73291015625}, {"start": 1710.72, "end": 1710.98, "word": " customer.", "probability": 0.68603515625}], "temperature": 1.0}, {"id": 68, "seek": 172667, "start": 1712.57, "end": 1726.67, "text": " Are we talking about a VIP customer? Are we talking about an ordinary customer? Are we talking about a rich customer? Are we talking about a poor customer? Are we talking about a manufacturer customer? Or are we talking about a government customer?", "tokens": [2014, 321, 1417, 466, 257, 29732, 5474, 30, 2014, 321, 1417, 466, 364, 10547, 5474, 30, 2014, 321, 1417, 466, 257, 4593, 5474, 30, 2014, 321, 1417, 466, 257, 4716, 5474, 30, 2014, 321, 1417, 466, 257, 18022, 5474, 30, 1610, 366, 321, 1417, 466, 257, 2463, 5474, 30], "avg_logprob": -0.17250000536441804, "compression_ratio": 2.5670103092783507, "no_speech_prob": 0.0, "words": [{"start": 1712.57, "end": 1712.83, "word": " Are", "probability": 0.853515625}, {"start": 1712.83, "end": 1712.97, "word": " we", "probability": 0.93408203125}, {"start": 1712.97, "end": 1713.27, "word": " talking", "probability": 0.8447265625}, {"start": 1713.27, "end": 1713.55, "word": " about", "probability": 0.9130859375}, {"start": 1713.55, "end": 1713.67, "word": " a", "probability": 0.20654296875}, {"start": 1713.67, "end": 1713.85, "word": " VIP", "probability": 0.8134765625}, {"start": 1713.85, "end": 1714.37, "word": " customer?", "probability": 0.77978515625}, {"start": 1714.89, "end": 1715.07, "word": " Are", "probability": 0.693359375}, {"start": 1715.07, "end": 1715.19, "word": " we", "probability": 0.962890625}, {"start": 1715.19, "end": 1715.43, "word": " talking", "probability": 0.86279296875}, {"start": 1715.43, "end": 1715.69, "word": " about", "probability": 0.90283203125}, {"start": 1715.69, "end": 1715.77, "word": " an", "probability": 0.69873046875}, {"start": 1715.77, "end": 1716.01, "word": " ordinary", "probability": 0.884765625}, {"start": 1716.01, "end": 1716.43, "word": " customer?", "probability": 0.7685546875}, {"start": 1716.89, "end": 1717.15, "word": " Are", "probability": 0.9423828125}, {"start": 1717.15, "end": 1717.29, "word": " we", "probability": 0.9619140625}, {"start": 1717.29, "end": 1717.53, "word": " talking", "probability": 0.853515625}, {"start": 1717.53, "end": 1717.83, "word": " about", "probability": 0.89453125}, {"start": 1717.83, "end": 1717.93, "word": " a", "probability": 0.96044921875}, {"start": 1717.93, "end": 1718.13, "word": " rich", "probability": 0.8427734375}, {"start": 1718.13, "end": 1718.55, "word": " customer?", "probability": 0.76708984375}, {"start": 1719.11, "end": 1719.39, "word": " Are", "probability": 0.92822265625}, {"start": 1719.39, "end": 1719.53, "word": " we", "probability": 0.9638671875}, {"start": 1719.53, "end": 1719.75, "word": " talking", "probability": 0.8603515625}, {"start": 1719.75, "end": 1720.01, "word": " about", "probability": 0.908203125}, {"start": 1720.01, "end": 1720.11, "word": " a", "probability": 0.96533203125}, {"start": 1720.11, "end": 1720.31, "word": " poor", "probability": 0.96630859375}, {"start": 1720.31, "end": 1720.73, "word": " customer?", "probability": 0.77685546875}, {"start": 1721.31, "end": 1721.51, "word": " Are", "probability": 0.951171875}, {"start": 1721.51, "end": 1721.69, "word": " we", "probability": 0.9638671875}, {"start": 1721.69, "end": 1721.91, "word": " talking", "probability": 0.87158203125}, {"start": 1721.91, "end": 1722.19, "word": " about", "probability": 0.89697265625}, {"start": 1722.19, "end": 1722.35, "word": " a", "probability": 0.94482421875}, {"start": 1722.35, "end": 1722.85, "word": " manufacturer", "probability": 0.78515625}, {"start": 1722.85, "end": 1723.37, "word": " customer?", "probability": 0.5263671875}, {"start": 1723.93, "end": 1724.05, "word": " Or", "probability": 0.8193359375}, {"start": 1724.05, "end": 1724.17, "word": " are", "probability": 0.89453125}, {"start": 1724.17, "end": 1724.45, "word": " we", "probability": 0.96337890625}, {"start": 1724.45, "end": 1724.73, "word": " talking", "probability": 0.875}, {"start": 1724.73, "end": 1725.05, "word": " about", "probability": 0.90380859375}, {"start": 1725.05, "end": 1725.27, "word": " a", "probability": 0.96875}, {"start": 1725.27, "end": 1725.61, "word": " government", "probability": 0.88671875}, {"start": 1725.61, "end": 1726.67, "word": " customer?", "probability": 0.64306640625}], "temperature": 1.0}, {"id": 69, "seek": 175222, "start": 1727.8, "end": 1752.22, "text": " And let's talk about this. So this type is used by companies as companies try to become more market oriented. So if you are going to make something called market segmentation, the third model, which is called customer specialization, is the best one to implement this thing. Is the best one to implement this market segmentation. Look at here. This is an example of the organizational structure.", "tokens": [400, 718, 311, 751, 466, 341, 13, 407, 341, 2010, 307, 1143, 538, 3431, 382, 3431, 853, 281, 1813, 544, 2142, 21841, 13, 407, 498, 291, 366, 516, 281, 652, 746, 1219, 2142, 9469, 399, 11, 264, 2636, 2316, 11, 597, 307, 1219, 5474, 2121, 2144, 11, 307, 264, 1151, 472, 281, 4445, 341, 551, 13, 1119, 264, 1151, 472, 281, 4445, 341, 2142, 9469, 399, 13, 2053, 412, 510, 13, 639, 307, 364, 1365, 295, 264, 24730, 3877, 13], "avg_logprob": -0.1876929071214464, "compression_ratio": 1.76, "no_speech_prob": 0.0, "words": [{"start": 1727.8, "end": 1728.0, "word": " And", "probability": 0.18359375}, {"start": 1728.0, "end": 1728.2, "word": " let's", "probability": 0.913330078125}, {"start": 1728.2, "end": 1728.38, "word": " talk", "probability": 0.88916015625}, {"start": 1728.38, "end": 1728.62, "word": " about", "probability": 0.90185546875}, {"start": 1728.62, "end": 1728.9, "word": " this.", "probability": 0.90966796875}, {"start": 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Listen, the customer here is a customer who is responsible for management of education and institutions. The customer here is a retail customer. The customer here is a wholesale customer. The customer here is a manufacturer. The customer here is a government. 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If you are going to hire a sales person for the government, this means this person knows all the people who are working within the different ministries. Ministry of education, ministry of supplies, ministry of transportation, ministry of telecommunication and so on. 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Not necessarily. Why? Because each salesperson has developed something called social relationship. This social relationship cannot be constructed in 24 hours. 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Why? 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Even a fattened purpose for this company is marketing. Because of this, sometimes this is going to what? To overcome the efforts and the goals which might be achieved by the marketing department within the same organization. Clear? Yes. 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And let's conclude this class. Here it is consistent with market driving strategy. This is advantage. Salesperson become customer experts. They know what the customer like and they know what the customer dislike. Even they know how the customers are taking their decisions of sales. Third, customer segments receive appropriate resources. All these are advantages. Fourth, customer specialist. 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Any question or comments about today's class? 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