Leveraging Retail Performance with Big Data

Retail industry is sitting on such a huge pile of all kinds of data that many others can only dream of. And this data does not require much effort to collect — every time the product bar code is scanned or a customer loyalty card is slid before purchase, loads of information go into retailer’s databases. The hard part is taking advantage of this massive data. And this is where present day data analytics technologies can greatly improve your bottom line.

Let’s give a closer look to some key areas for data analytics application.

Forecasting demand

Demand is probably one of the most obvious and critical components of retailer’s success. Balancing prevention of shortages and avoidance of inventory pile-ups is an art where the science of data can be a life-changer. When the new product is going to be released, how should marketers find out who wants it the most, where the largest clusters of target customers reside, and when is the best time for the launch?

Data analysis is all about finding dependencies and patterns. To help predict demand the following … may be insightful:

Customer-based

· Demographics & psychographics: Who buys/wants your product? Men or women? Of which age bracket? With or without higher education? Single or married? With or without children? With or without pets? With which level of income? Using cars or public transport?

· Social media: In what context is your product mentioned? What are the interests/hobbies/backgrounds of people talking about it?

Company-based

· Distribution: Which channels are effective? Which stages in supply chain contribute most to the delays? Is this seasonal?

· Marketing: How many sales/new customers/likes did the ad campaign generate? In which regions was it more effective? Why? (related to demographics)

Market-based

· Competition: Is there correlation between competitor lowering prices and drop in your sales?

Environment-based

· Climate: Which weather conditions correspond to increased sales?

· Social: Do sales peak around holidays? Which holidays? How sensitive are sales to increase/decrease in disposable income? Do current/emerging trends in society influence sales in any way?

As you can imagine, there can be virtually unlimited number of combinations of these factors. Finding the relevant ones, grouping them appropriately, drawing insights, and initiating actions — these are the consecutive steps which properly and thoroughly analyzed data can guide you through.

Warehousing

Keeping track of your inventory is critical to smooth operation of business and satisfied customers. The ability to forecast lead times, understand causes of delays, and optimize the supply process leads to huge savings resulting from reduced cost of storing inventory, fewer stock-out days, and more retained customers.

Long-term analyses can reveal which products or product categories have a declining demand trend and which are the new fads requiring top-priority procurement and more shelf space.

Reasonably managing available space with the use of up-to-date analytical and visualization tools vastly improved operational performance at an incomparably low cost.

Personalisation

Big volumes of data allow for better customization of company offerings. Past customer purchases let you segment your client base to the point of having 1-customer segments.

Offering complementary products can be smart! If your client bought a pair of shoes a week ago, offering another shoes is foolish. More suitable options include shoe polish, new laces, or a matching belt. If you purchased a trip, you don’t need another trip, while sunglasses, suitcases or GPS navigators can be relevant. If you also take into account the season of the trip, you may want to offer a snowboard, thermal clothes or a GoPro cam.

Apart from product offerings, data can help you in adjusting prices. Generating the price for each customer dynamically, based on their browsing history, previous purchases, personal details (demographics, region, etc.), may attract more customers, increase sales and still maintain the same average selling price. Some airlines increase prices for their tickets if they see that the person has checked seats availability several times within previous couple of days, because this indicates their interest, and most likely lack of alternatives. On the other hand, if a young couple is looking for a stroller on your site and you see that they stick to the cheaper models, you may offer a lower price for a more expensive model and thus seduce them to part with more money while getting a good deal.

Conclusion

In the present-day ever-changing world analytics has to be continuous in order to be relevant. Being a second late deprives you of a chance to cross-sell an additional product, offer localized offerings by tracking the customer location, or customize the price based on customer behavior.

Use of cutting-edge solutions fine-tuned for the needs of your business and your industry are vital for achieving significant tangible results. High-performance programming languages having extensive data analytics functionality (e.g., Python, R) coupled with efficient databases capable of juggling millions of rows of data (e.g., Hadoop ecosystem) and framed with visualization tools for crisp and intuitive representation of results (e.g., Tableau) give limitless opportunities to business growth and prosperity while maintaining low scalability costs.

Data in itself if useless, distilling meaning from data is an art. Entrust creation of the masterpiece to seasoned and equipped professionals.