Diversifying Product Lines With Analytics

The use of analytics could change product strategies forever


When a business is faced with disruption from insurgents utilizing more advanced technology, there are a number of strategies that can be employed to maintain market position. Many close their eyes and simply choose to believe consumer habits working against them is a temporary blip and do nothing as a result. Blockbuster could be taken as an example of such a company, having been caught seemingly unawares by consumer desire to stream movies online. It closed the doors on its last 300 retail stores in 2013.

While failure to keep up with online streaming is often cited as the reason Blockbusters went bust, according to Jonathan Salem Baskin, a former Blockbuster executive, the story is actually that of a more complex failure to diversify. Baskin wrote in Forbes that, when faced with falling traffic back in the 1990s, ‘the company elected to focus on upping the value of each transaction basket. This meant filling the stores with lots of candy, throw-away toys, and other impulse purchase items, displayed at little kid height so parents would be forced to buy it.’ Baskin argues that this missed the point of Blockbusters’ core reason for being - that its revenue relied on renting out box office hits and people did not necessarily enjoy the experience of going to the store. He argues instead that the solution should have been ‘to focus on consultative or advisory selling, and turn its store associates into de facto recommenders. It could have implemented true social networks to rate and catalog movies, and used its customer data to develop a predictive engine that members could use to locate new titles.’

Blockbuster’s failure, ultimately, was the result of it not understanding its business model and what additional products its consumers would buy from it. History is filled with company’s failed attempts to diversify their product lines in order to drive growth. Diversification is unpredictable and high-risk. There are many things to consider, such as understanding consumer desire for your new product, whether it fits in with your brand, and what kind of losses are acceptable. While complicated, however, it is a necessity. The ‘product portfolio’ concept states that if a company is going to grow while also allocating resources wisely, it must mix established with new businesses. This is especially true in the digital age. 52% of the firms that made up the Fortune 500 at the turn of the millennium have now gone because they failed to make the digital shift.

Data analytics goes a long way to lowering the risk associated with diversification. It enables companies to better gauge consumer demand for new product lines, applying sentiment analysis to unstructured data in social media and consumer surveys. It provides better analysis of the market, and what kind of losses are to be expected. It is usually assumed that expansion of any business will lead to losses, but the depth and length can make or break whether or not it is a worthwhile venture.

Starbucks is a recent example of a company that has used data to expand its product portfolio. It talked to its baristas about how customers ordered coffee, lattes, and tea in-store, and blended this information with several industry reports about at-home consumption. For example, data from consumer research firm Mintel found that 43% of the tea-drinking customers don’t use sugar, while 25% of consumers don't add milk to their iced coffee when drinking the beverage at home. To satisfy such demand, they created unsweetened and sweetened black iced coffee without milk or added flavors to sell in grocery stores, outside their core coffee shop business.

Ultimately, data analytics enables better decision making. There are few areas in business riskier and more complex than diversifying product lines, and to put in place the best strategy to do it, analytics is a vital tool.

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