This is a user generated content for MyStory, a YourStory initiative to enable its community to contribute and have their voices heard. The views and writings here reflect that of the author and not of YourStory.
Retail industry can use predictive analytics to get market, business and millennial intelligent
This is a user generated content for MyStory, a YourStory initiative to enable its community to contribute and have their voices heard. The views and writings here reflect that of the author and not of YourStory.
Customers upon receiving a notification from the supermarket indicating their sugar or any other grocery item is about to finish and if they need some more; know for sure that the retailer is not into reading minds, instead, it is just predictive analytics algorithms processing big data patterns.
The retail industry, of course, due to its nature, pioneered the use of data and its sub-disciplines. Same is the case with predictive analytics. It leverages data to enable retailers to plan their business and marketing strategies, replenishment management, inventory in addition to minimized risks and uncertainty.
Here are some of the ways how retail industry uses predictive analytics not only for market & business intelligence but also to woo millennials as well:
To get millennial intelligent, retailers should think like one
Millennials, those born between the 80s to early 2000s, are the biggest demographic for retailers. It may be due to the reason that their needs are unlike any other generation before them. Marketing or wooing this 80-million–strong population, happens to be one of the biggest challenges for the retail industry.
The concept of personalization has been quite simple for those hundreds of thousands of customers and the retailers who sell to them, but somehow it gets complicated when it comes to millennials. Thanks to the digital footprint left by millennials. Data collection, cleansing and using it for analytics is done by retailers to use it further for machine learning. They now can create and leverage insights derived thus, to create customer segments and push personalized marketing.
• Behavioral patterns Vs traditional marketing segments
As we suggested, retailers need to stop thinking of millennials as an entire market; but break them into segments. Identify how he or she makes purchasing decisions, their specific needs and wants etc. Retailers collecting data at every single touch point, smart algorithms get enlightened with details about customer purchase history, patterns, preferences, page views, interests and several other forms of engagements, and all this for creating a single customer view. Further value addition is done by predictive analytics by empowering retailers to foresee what their customer’s next move would be and make recommendations about relevant products purely based on their behavior, accordingly.
Customers are always appreciative of relevant suggestions customized to their needs, and hence such recommendations increase customer engagement and brand loyalty.
• Millennials are unique
A listed result upon typing just a few letters is something everyone loves. Predictive site search is what makes customers have a user-friendly shopping experience, based on customer history, behavior and preference, better than ever. Customer experience being one of the most important assets for retailers, as it can predict what the customer is looking for, it is extensively used to gain customer satisfaction and loyalty.
Market & Business Intelligence
The science of data and analytics has debunked the myth that retail business is an art of winning over customers. Retailers are succeeding with help of market and business intelligence reports, when it comes to assessing, testing and planning their business strategies.
Furthermore, retailers are actively utilizing structured and unstructured data about customer behavior and clustering techniques to help improve performance.
• Deciding on shop location
One of the most strategic decisions, with everlasting effect, in the retail industry is choosing a store location. Predictive analytics puts at task demographics, property market, competitive activity, market conditions, customer purchase power, purchase behaviors etc. to forecast potential revenue for any selected store location before dollars are invested. Said this, predictive analytic algorithms also help in analyzing and managing existing locations.
• Aid in setting the prices
Accurate collation of demand, product pricing history, competitor activities and inventory levels; is the key to predictive pricing analytics. It helps in automatically setting optimal prices, facilitating prompt and appropriate response to market changes in real-time.
Not only contemporary but predictive analytics help the conventional approach as well. The traditional “season sale” approach where the demand has already died off, predictive analytics determine the optimum time when prices should be dropped. It has proved its worth by portraying gradual reduction in price generally leads to deliver maximum profits.
• Smart revenue forecasting
Demographics, market insight, response rates and geography are what predictive algorithms use for data collection and analytics. Marketers pinpoint most effective messages/products for a single customer, and all this by determining what campaigns would be more successful based on analytics. Running targeted marketing campaigns leads retailers to attain elevated conversion rates.
• Inventory management
Apart from detecting customer behavior, predictive analytics is potent to track economic indicators, market dynamics, discounts and allocations among stores to optimize stock management and supply chain. It enables retailers to allocate the right products – to right stores- at the right time. This helps them in avoiding product waste.
Final word
Predictive analytics can help several other areas in the retail industry; but only if it is applied and adhered to thoroughly. Benefiting the retailers’ big time, it enables organizations to plan their business from every aspect while responding to market changes – quickly. However; the humongous amount of customer data and the wide plethora of permutation and combinations that follow, are usually unmanageable for in-house business excellence teams. The need of the hour is to scale data analytics and distribute its benefits company-wide, to reap hundreds of millions of dollars in annual revenue increase and prominent margin improvements. Integrating predictive analytics in both online and offline channels for the big picture and practice Omni-channel strategies, is important for the retailers and brands to not only survive but thrive in the global market.