{"id":143,"date":"2020-06-04T14:35:54","date_gmt":"2020-06-04T09:05:54","guid":{"rendered":"http:\/\/3nayan.in\/blog\/?p=143"},"modified":"2020-06-04T14:35:54","modified_gmt":"2020-06-04T09:05:54","slug":"sales-data-in-fmcg-lags-in-analytical-capability","status":"publish","type":"post","link":"https:\/\/3nayan.in\/blog\/2020\/06\/04\/sales-data-in-fmcg-lags-in-analytical-capability\/","title":{"rendered":"Sales Data, in FMCG, lags in Analytical Capability"},"content":{"rendered":"\n<p><em>In the <a href=\"http:\/\/3nayan.in\/blog\/?p=84\">previous two articles<\/a> of the series, <strong>Vivek Bhatnagar<\/strong> wrote about evolution of data and the need for analysis. In this article, he poses questions about how far the data can be used.<\/em><\/p>\n\n\n\n<p>In my previous two articles related to Data in FMCG, I had written about the plethora of available data and how one could start using the data. Lets take a step forward, and look at the future. Consider for a moment (figure below) how FMCG Sales Data has evolved.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"625\" height=\"328\" src=\"http:\/\/3nayan.in\/blog\/wp-content\/uploads\/2020\/05\/image-8.png\" alt=\"\" class=\"wp-image-91\" srcset=\"https:\/\/3nayan.in\/blog\/wp-content\/uploads\/2020\/05\/image-8.png 625w, https:\/\/3nayan.in\/blog\/wp-content\/uploads\/2020\/05\/image-8-300x157.png 300w\" sizes=\"auto, (max-width: 625px) 100vw, 625px\" \/><figcaption>Evolution of Sales Data in FMCG<\/figcaption><\/figure>\n\n\n\n<p>Two decades ago, Primary Sales data apart, just having a duly filled DSR was referred to as Sales Data lending itself to&nbsp; rudimentary analysis at the local distributor level. While today, we generate enormous quantities of data enabling answers to any sales related query all the way down to the SKU level at a shop.<\/p>\n\n\n\n<p>But the use of the data still lags from its true potential. &nbsp;I see data being used today to generate cumbersome set of vanilla MIS reports, or ad-hoc reports for senior management (for their meetings) or maybe to solve a local territory or salesperson level issue<\/p>\n\n\n\n<p>All these only obliquely address the main function of generating insights to make data based decisions.&nbsp; That too, not to its potential.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-style-large is-layout-flow wp-block-quote-is-layout-flow\"><p>3nayan finds that organizations often overlook the wide variability in their data and what it takes to transform it to actionable insight<\/p><\/blockquote>\n\n\n\n<p>In fact, the approach of Case to Case analytics and pre formatted reports are both a good to have, there is much more that this data can do. <\/p>\n\n\n\n<p>Consider two sets of actions:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Create a real Data Analytics Team, with Data Scientists, Data Analysts etc. Not your usual MIS type. If you can\u2019t create the team, outsource it but please &nbsp;don\u2019t let Data lie around because you don\u2019t know what to do with it.&nbsp;<\/li><li>Besides, one could go ahead and host your data on any of these cloud providers where you can use contemporary visualization, AI \/ ML to bring forth meaningful insights and make predictions. Allow me to show you an art-of-the-possible type of example, a rather simple one. Consider a grocery chain which sells fresh, and also frozen. The chain collects data of sales, how much inventory, how much lag time between order and materials being on the shelves etc. Let\u2019s have them also collect other, somewhat unstructured data around weather, traffic conditions using maps and availability of trucks from a 3PL company and the varying durations it takes for mangoes and ice-creams to arrive. It senses over time that there are micro-seasonalities and correlations of sales between Mangoes, and ice-cream (vanilla and mango flavours) and weather. Should the intelligent engines understand this, then they could trigger off a buy \/ replenish request to have enough store based inventory in regions where the temperature is forecasted to go up a certain level. The complexity that can be crossed is that the purchases would be made keeping in mind the likely requirement dates, and the current supply and logistics situations. Further possible complexity simplification would be doing a trade off with the current price in the wholesale market (for mangoes) vs, storing them in a cold room (in the store).<\/li><\/ul>\n\n\n\n<p>But, do we really see data analytics being used to this level? In my FMCG experience, not really. I see this situation, not because of a lack in technology, but more in the ability to consume the data meaningfully. Maybe:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>The decision makers for this required Digital Transformation are themselves not abreast with the latest?<\/li><li>The benefits of analytics are not fully appreciated? Or do people know what appropriate question to ask of the data?<\/li><\/ul>\n\n\n\n<p>The assumed costs of transformation and potential ROI is not clear?<\/p>\n\n\n\n<p>The possibilities with deep analysis of data are absolutely immense.&nbsp; The decision makers need get abreast of what is possible, get future oriented, start strategising and chart their journeys. The current world situation provides an unprecedented opportunity which will be a shame to waste.<\/p>\n\n\n\n<p class=\"has-small-font-size\"><em>How far have you analyzed the data that you gather? Are you able to make changes in your processes using the data and creating an uptick in your revenue? Could we have a discussion on how that could be made possible? Visit the <a href=\"http:\/\/3nayan.in\/\">3nayan web site<\/a> or <a href=\"mailto:reach@3nayan.in\">write to us<\/a>.<\/em><\/p>\n\n\n\n<p class=\"has-text-color has-vivid-cyan-blue-color\"><em><a href=\"https:\/\/www.linkedin.com\/in\/vivbhatnagar\/\"><strong>Vivek Bhatnagar<\/strong><\/a> is an FMCG veteran in India, and is a partner at <a href=\"https:\/\/zaconsulting.in\/\">ZA Consulting<\/a> which provides business consulting services India in areas of sales and distribution, new product launch and go-to market strategies. ZA Consulting is a <a href=\"http:\/\/3nayan.in\/\">3nayan<\/a> partner.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This article, part of a three article series, talks about how data can be used in FMCG business, especially at the distributor sales point. <\/p>\n","protected":false},"author":5,"featured_media":151,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12,13,19,20,6],"tags":[61,74,79,84,85,110],"class_list":["post-143","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","category-analytics-strategy","category-data","category-digital","category-retail-and-consumer","tag-business-intelligence","tag-consumer-goods","tag-cpg","tag-data","tag-data-analytics","tag-fmcg"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Sales Data, in FMCG, lags in Analytical Capability - 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