All consumer goods companies typically have very robust analytical capabilities and mine their data warehouses extensively. They use their supply chain and sales data in order to manage their costs and stay competitive in a difficult external environment. Until now, they have only been exposed to taking advantage of the enterprise data that is already captured in their internal systems. Today, technology has made it possible for them to have access to a continuous stream of information from external sources, including point-of-sale data from retail outlets and customer sentiments and other data from social media sources.
This treasure trove of disparate and external data can effectively be used by companies to gain a competitive advantage and further profit. In this article, I am going to highlight the potential of using analytical tools to make business sense of three critical sources of external data in the consumer goods industry. The first is trade data or data from retailers. This set of data is often, not captured or even if captured, it is not used effectively. The second source is sales & supply data. This data is available but not combined with other information to derive real time value. The final source is sentiment data retrieved from social media and online sources.
Big data from external sources
Trade data is external to a consumer goods enterprise. It includes sales data from retail outlets, market measurement data, retailer information data, and competitor sales estimates. The volume of this data translates to terabytes of data every day and most companies are trying to figure out how to take advantage of these varied data sources together at scale (Walmart alone sends data every day for every SKU for every store, and is considering moving this to hourly!).
The answer to this question can be found in Big Data analytics. The first use case for big data analytics is in trade promotion optimization. Every consumer goods company pays retailers, to put their product on the shelf and these payments are called trade promotions.
Now consider this scenario, what if the marketing team at a consumer goods enterprise had an analytics system that pulls in daily sales data from specific stores to reveal how a test product was selling during the first few days of launch. The marketing team could then place the product in different sections of the store (near the check-out counter, in the product section or create special counters or kiosks) to find out which placement is able to draw maximum customer attention. This information from multiple stores can be assessed to change the location of a product in all stores and improve the success rate of new products.
Key Account Managers and Sales Managers can also further the daily sales data to run profitable promotions and figure out if retailers actually implemented the promotion or not. Figuring out how to spend as little as possible to get your product in the best position on the shelf with the right displays and coupons can potentially save billions for an enterprise.
Multiple products in a category tend to cannibalize each other, and big data analytics can be used to estimate an optimal mix of products to steal market share away from competitors while limiting cannibalization and maximizing profits. For example, if Haldiram’s looks at a few years’ worth of sales data for historical product mixes in retail outlets and compare those to competitor sales, it can determine a minimal product mix designed to grab as much market share as possible with the fewest of products.
Sales and Supply Data
Sales, production and supply chain data in general is owned data that lives inside the company firewall and probably in their data warehouses. However, looking at this data swiftly and looking at it across all of the data types is very difficult inside the warehouse because of different hierarchies involved (product hierarchies, customer hierarchies, etc.). Big data can do this on the fly.
For example, if sales promotions are not being executed as planned; it can throw off demand estimates and the company can end up running out of product due to overselling or having extra inventory that takes up warehouse space. Both situations reduce profit margins.
Supply managers can use analytics to derive more accurate projections of future sales or modify a plan mid-way. They can avoid having hundreds of thousands of extra cases in a warehouse and save millions. The same data turned around the other way can be used by marketing to help increase sales to match up with production. Marketing can use the analysis to run new campaigns or cancel campaigns in order to speed up or reduce sale of products.
Sentiment and Social Media Analysis
A consumer goods enterprise must be the last business to ignore the emergence of a social consumer. Passionate expressions of consumer opinion on online platforms about a product or a campaign can significantly affect the sales.
Big data solutions can help companies collect or stream data from social media sites directly into their customer relationship management (CRM) or customer service applications. This will help them determine the relative importance of a particular social media post; determine the clout of a customer and get a better picture of his/her behavior. It can further help integrate new channels such as social media and mobile into the marketing mix to attract and engage with consumers.
For example Facebook posts and posts from brand sites can be analyzed after a new product is launched to get an idea of frequent likes and dislikes about how the product was marketed and where people are buying it from.
Marketing can use this information to change the marketing campaign, especially the electronic portions mid-launch. They can focus on positive aspects of the product that are generating a lot of buzz or cancel an ad campaign that’d drawing negative review from consumers.
Social media analysis can also help understand competition. A company can use the information to better position their own products by differentiating their offering from the limitations and perceived quality of a competing product.
The consumer goods industry in India as well as globally, has been growing at a brisk pace. India has been called out as one of the biggest emerging markets for the consumer goods industry. In a constantly changing ecosystem of retailers and customers – consumer goods manufacturers must make business sense of the critical data being generated by these two important stakeholders. Investments in technology solutions like Big Data are therefore vital to grow the business and stay profitable.