“Over the last couple of years, we’ve observed two significant changes in analytics: information has become intelligent – it can be modified to fulfill the needs of every seeker, context and circumstance – and analytics has succeeded in ‘closing the loop’ and become truly actionable,” says Atul Jalan, CEO and Managing Director, Manthan.
These were Atul’s opening thoughts when he began an intriguing conversation with Brian Kilcourse from RSR Research at National Retail Federation (NRF) 2014. At the NRF 2014 Big Ideas Session titled ‘The Power of Do –Analytics-driven assortment and price-tuning’, Atul spoke about the altering role of analytics in retail and its transformation from being a decision-support system to a decision-making system, to a decision-execution system.His key thoughts are great takeaways for retailers looking to understand the emerging changes and latest concepts in retail analytics and enhance the use of analytics for greater business success in today’s changing environment.
Move towards actionable analytics
Initially analytics was about automation of business processes. Then it evolved into the intelligent driver of underlying business automation systems and processes. Today, business analytics offers a three-fold approach to retail decision-making:
- Discover associations that are subterranean or counter-intuitive through data and pattern recognition. This implies that the associations like in the famous beer and diaper story are becoming every-day discoveries.
- Predict the outcome of decision-making and help arrive at an optimal decision.
- Close the loop by implementing the optimal decision.
The ability to close the loop is the fundamental change that has occurred in the last few years. Information has become intelligent and analytics has become actionable. The ability to act on insights is now the center of gravity for analytics. And playing on this leading edge, Manthan has embedded the strategy ‘analyze, decide, do’ at the core of their innovation philosophy, and therefore their solutions.
If there is a gap between the insights that analytics provides and the retailer’s ability to act upon these, the opportunity is often lost. So understanding the data, using the data to plan an action, and implementing the planned action quickly are the three sides of analytical decision-making. For example, during price markdown, analytics must provide insights, options and perspectives on the decisions retailers can take in the terms of price optimization. That is the analysis part. Then simulating the outcome, calibrating each decision with business constrains, and arriving at the optimal decision is the decision-making part. After the decision about price optimization is made, organizational realities come into play. Price is usually the domain of the account group and the business group is responsible for implementing the new prices. So to help implement the decision, Manthan has introduced some very interesting, innovative and creative tools, like collaborative workflows within the analytical process, which can be used to share the new price, get it reviewed or approved, and distribute the price right down to the underlying price management system, making a real business-process change in real time.
Demonstrating the success of ‘do’, Manthan has improved the number of price changes that a tier-one cosmetic company used. The number of new price advice that this company generates today has moved up by 300 percent. While operating the price center of excellence for the cosmetic company, Manthan not only provides the best price that mathematics can calculate, but also helps the company, in real time, to calibrate these mathematically decided prices with the constraints and the context of their business.
Catch non-transactional data signals
Shopper habits are changing alongside the consumer mindset. The multiplicity of channels has provided new data points to retailers as opposed to the traditional POS approach. Today, social media, mobile technology, and online platforms have opened new opportunities to understand the customer beyond transactions, to know the sentiment, to get a market sense of preferences and demands. Retailers are able to drive personalized marketing and merchandising, anytime-anywhere channel-agnostic retailing, contextual and location-based marketing, and predictive, prescriptive and proactive customer engagements.
Social media is a great testing and learning platform for new product launches, assortments, and marketing initiatives. Sentiment analysis has been used increasingly to fine-tune campaigns for better results. Today retailers do not need to choose between two options based on customer preferences, but need to offer both based on purchase history, web history, social graphs and more.
Manthan has been advising retailers to use social media in a three-step way: to announce product launches, to involve customers in the product design process, and to make pre-launch offers. The responses to the pre-launch offer or the involvement in the design process work as a very robust data mimic to understand consumer preferences and demand which then can be used to drive assortment decisions.
Get a 360-degree view of your customer
Today’s retailer needs clear insights on the entire shopping journey of the customer. The goal is to understand the customer as closely as mathematically possible, including understanding purchase history, customer segment, churn propensity, and thus arrive at the loyalty quotient of each customer.When retailers recognize the uniqueness of the consumer and match it with the uniqueness of their business, they can drive personalization more effectively.
Manthan allows their retail customers to use a Manthan mobility application. The retailers then distribute it among their consumers and deliver analytically driven promotions and offers in the context of the customer.
Recognize the importance of dynamic pricing
The days of a perfect price for a category or an item, irrespective of who is looking at it, are over. We are truly in the era of ‘so-lo-mo-me’ – social local mobile and me, that is, personalization.
The pricing today is mathematically strained and dynamically personalized. Dynamic pricing is dependent on four analytical models: the customer perspective, which includes the economic value of the customer and the life-time value or loyalty quotient, market perspective gained from sentiment analysis and social media, the cost-to-serve understanding including supply chain and inventory cost, and external factors such as the weather, season, and macro-economic conditions. All these perspectives must be layered accurately to arrive at personalized and dynamic pricing.
Ensure complete item visibility for a successful ‘endless aisle’ initiative
The phenomenon of online shopping has compelled retailers to introduce new concepts. Many stores are implementing in-store kiosks that allow customers to order products that are not in the store and have those shipped to their homes. This approach needs a deep understanding of many aspects – the channel of demand, customers’ path to purchase, products they purchase online as opposed to products they buy in the store, the expected point of fulfillment. The new supply chain enables retailers to use direct drop shipment to customers’ homes.
For a successful endless aisle initiative, retailers need a data infrastructure that gives them a complete view of their inventory and items, regardless of their physical location –in the store or in the warehouse, or in the supplier’s warehouse. Item-level visibility of data is crucial for the success of such initiatives.
Concluding the conversation on an optimistic note, Atul says, “The entire dynamics of online retail, social commerce, and social media has shaken things up in a positive way. It is a wonderful opportunity for retailers to truly rethink and remodel their businesses, with the support of analytics.”