Affine recently completed 6 years, I have been a part of it for about 3 of those years. As an analytics firm, the most common business problem that we have come across is that of forecasting consumer demand. This is particularly true for Retail and CPG clients.
Over the last few years have dealt with simple forecasting problems for which we can use very simple time-series forecasting techniques like ARIMA and ARIMAX or even linear regression these are forecasts which are more at an organization or for specific business divisions. But over the years we have seen a distinct shift in focus of all our clients to get forecasts at a more granular level, sometimes for even specific items. These forecasts are difficult to attain using simple techniques. This is where more sophisticated techniques come into play. These techniques are the more complex machine learning techniques which include RF, XG Boost etc.
We cater to various industry domains and verticals and to explain how the clients’ requirements have changed over the years I can think of two very distinct examples from two specialty domains, video gaming industry and sportswear manufacturer and retailer. Below I will try to explain how the business requirement for a forecast was different for both these clients.
Over the last few years, the popularity of one franchise belonging to the publisher has gone down due to various factors. The stakeholders wanted to understand the demand pattern for the franchise going forward and they wanted monthly predictions for the franchise’s sales for the next 1 fiscal year. This franchise contributed to almost 60% – 70% of the organization’s revenue and we were required to predict the sales for only this franchise. Also since this was a month level forecast/prediction we had which are primarily black box techniques being appropriate for this requirement. enough data points to use either a time series analysis or even a regression analysis to predict sales. We tried both and finalized on a regression-based analysis so that we could also identify the drivers for sales and their impact which was important for the stakeholders.
In the case of the sportswear manufacturer and retailer client, they wanted weekly forecasts for all the styles available in their portfolio. Hence the client required predictions for all the items available for all the weeks in a fiscal year.
There are a lot of items here which are newly launched items having very few data points at a week level. Here, the traditional time-series methodology will fail because of lack of data points, also not all the styles will showcase similar trend and seasonality. Along with this, there will also be styles which have minimal sales and prediction for these styles is a major challenge for this client. We had to develop an ensemble of models where we divided all the styles into few buckets of
For the styles that have high volume and high duration, we can still use a time-series or a regression technique but for all the others these traditional methods will have limitations. Hence, we needed to apply ML techniques for these styles.
For the styles with low duration, we used Random forest and XGB methods to arrive at the predictions. Also for these styles what was more important was to get a proper demand prediction rather than identify the drivers of sales and their impact, hence ML techniques.
As an analytics practitioner, we recognize that there is no one-size-fits-all approach to data analysis. To identify the best approach, one needs to have a deep knowledge and practical experience in various approaches. This was established in our recent experiences. While the video game publisher’s requirement was primarily for an entire franchise for which we could use simple time series and regression techniques, the sportswear retailer’s requirement was much more granular. In another experience with the sportswear retailer, item level predictions were the prime requirement and over time we as an analytics firm have seen this change in requirement from an overall demand prediction too much more granular predictions across the board. Also, most of our clients tend to make informed decisions about how much inventory to produce and stock for each item and a granular prediction at an item level aids that.
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