Algorithmic Retailing- Connecting Big Data with the rhythm of customer pulse

Big Data and Algorithms

“Data is inherently dumb. It doesn’t actually do anything unless you know how to use it; how to act with it. Dynamic algorithms are the core of new customer interactions.”- Peter Sondergaard, senior vice president at Gartner and global head of Research.

Let’s take the example of how our brain functions, the information is collected through various channels like sensory organs and stored. Then the stored data is analyzed and the neuro networks then put this analyzed data to use, for example How to solve a mathematical equation? How to formulate a plan? How to find solutions to complex issues? Big data not only presents a huge amount of unstructured data, but it also poses a challenge, what to do with this data and how to use it effectively? Dynamic algorithms, placed strategically open doors of opportunity in the accumulated unstructured big data to channelize the right information at the right time. Big data alone does not possess the ability to interpret information to find insights. Thus the retail scenario is employing algorithms and machine learning to get the insights to stay ahead in the market.

Increasing Need for Algorithms in Retail Scenario

From prediction to prescription to planning, retailers are strategically applying complex algorithms to various business areas, why? To capture the attention of the ever evolving customer base who are leading the innovation in the market. Customers, who are used to getting services at their fingertips and products at their doorsteps, are becoming harder to please each day.

The need for algorithmic retailing is growing with the times as the competition increases and the average customers’ preferences change quickly with technology penetration and increasing options. The trend is propelling businesses like grocery and staples to big fashion brands, to apply algorithms in numerous ways to make faster business decisions

Did you know?

Gartner describes algorithmic retailing as “Algorithmic retailing is the application of big data through advanced analytics across an increasingly complex and detailed retail structure to deliver an efficient and flexible, yet unified, customer experience.”

Using Algorithmic Retailing at the right place at the right time

Applying the “if then” logic, retailers can comprehend the market’s volatility and be prepared for the same. Algorithmic retailing, specifically in the areas of predictive analytics in retail, retail demand forecasting and Omni channel retailing are aiding businesses in problem solving and faster decision making by providing the right logic at the right time. Here are some examples of the various use cases of applying algorithms for harnessing and channelizing the data for driving business success.

Inventory Management

One of the biggest challenges faced by retailers is inventory management. Using algorithmic retailing one can stay a step ahead with predictive analytics in retail which can solve inventory challenges. Predictive analytics in retail is algorithm applied to predict future market demands and fluctuations. This helps in stocking up the right products according to the situation that would help generate more business. Take the example of Walmart before hurricane Frances hit Florida. Walmart had analyzed huge amount of stored data in order to find out customer behavior before and after the last hurricane had struck. They stocked up accordingly and made good business.

Store Display

Applying algorithms in retail to analyze and forecast the trends informs the store managers about how to strategize their store and window display. For example in fashion if most well-known designers are using crimson and yellow hues for the fall, fashioning your clothing store window display using the hues would make it more attractive to buyers, increasing the footfall. It also increases the brand value as it positions you among the trend setters being up to date with your industry.

In Store Allocation

Businesses have become time sensitive across industries and retail is no stranger to real time scenarios. The power remains in knowing what your customer wants ahead of your customers. Applying algorithms to big data will help in building intelligent predictive models that would equip you with the knowledge found from unstructured data, collected from various sources (social mention, buyer history, preference etc.) with which you can measure the footsteps of your customers. So if it’s winter season, according to geo location, weather condition and pattern of buying activity your grocery store can plan allocation of supplies. For example, fresh veggies at the front, then bread spreads and then beer etc. You are actually able to predict what your customers will buy in which order, making the store easily navigable and the shopping experience smoother and less time consuming.

Targeted Marketing

There are too many examples of online retailers exploiting the big data potential with algorithmic retailing for targeted marketing. Take the example of Amazon or Alibaba. They provide real time recommendation based on browsing activity and send activity based recommendation texts and mails and provide advertisement on social media as well. Instead of generalized targeting depending on geo location or age and sex, algorithmic retailing takes into account a greater number of variables (market, influencers, social activity, shopping history etc.) to give a more accurate view of customer preference, making targeted marketing successful. By placing algorithms online and by static reports retailers can find out characteristics of longer lifetime value customers and curate offers catering to their needs and bring better value to business.

Geo Fencing

Using Beacons and sensors to collected geographic data has been in practice for quite some time now. But to make good use of the collected data and to channelize it into geo fencing based marketing requires advanced analytics in retail based on algorithms. To ensure when an existing or potential customer walks in the fenced area, you are able to send curated messages to him/her. Walmart’s store mode allows for handheld devices to respond to geo fencing when around a store. They offer coupons and e-receipts via message to encourage the customer to spend more time in store.

Channel Optimization

Connecting the dots for IoT is algorithmic retailing. For the smart phone yielding, app and website friendly customers, channel optimization for a retailer is a must. Putting the theory to practice is Macy’s, with the app helping people find their way inside store, compare products, order online pick up instore or get door step delivery and also pick up in case of return. Comparing inventory availability and in store stock, calculating expected delivery time, infusing online activity with in store to find similarities and provide instantly redeemable offers, algorithmic retailing is doing it all behind the scenes.

From optimizing prices depending on the market scenario to market basket analytics, retail hardly has a segment left where algorithms are not at work yielding strong results. Algorithms are being applied to in store traffic pattern measurement and will continue to explore new territories of retail in future allowing for better decision making.

What the future holds

As per Gartner, merchant leaders will use algorithms by 2020, prompting the top 10 retailers to cut up to one-third of headquarters merchandising staff.

As the forecast clearly shows that algorithm retailing will keep on influencing the market in the future and customers would be presented with an ever evolving retail scenario where the services are personalized and optimized. The dynamically evolving retail market will also have positive effects on the changing economic scenario as both the retailers and the customers become wiser with their choices.

Author : Shreyasee Ghosh, Research Analyst

Practice Head: Raj Bala, Big Data