Next stage of SFA through Machine Learning & Artificial Intelligence in FMCG/ retail

No wonder Amazon attributes its business growth to the very powerful technology capability powered by ML & AI.
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Everyone with a shopping experience on Amazon must have noticed relevant product suggestions, which are very refined and personalised. Sounds familiar?

The credit goes to advance Machine Learning (ML) & Artificial intelligence (AI) algorithms which are capable of churning big data and deliver such beautiful buying experience. No wonder Amazon attributes its business growth to the very powerful technology capability powered by ML & AI. Such success stories are ubiquitous in the new digital ways of business management.

The question rises, if the FMCG and retail industry, can leverage ML & AI to deliver better business effectiveness and sophisticated user experience. The answer is an emphatic YES and anyone who is sceptical about this may have to wake from a regretful slumber.

While its application can enhance the length and breadth of sales automation, here are the “Top- 5” use cases which FMCG and retail sector is focussing on to drive the next stage of growth

  1. Order Recommendation: A typical salesman spends approx 5-8 mins in a store with 100+ products in his catalogue to push. Left to the salesman intelligence may not yield the desired growth.ML & AI helps suggest the right product assortment to maximise the sales in retail. It takes all the right ingredients to propose the sales order, historical purchase data, must sell SKUs, promotions, returns and similar store purchase behaviour. It’s essentially helping salesman to SELL MORE SELL BETTER Successful implementation can lead to an average of 8-10% same store growth (either lines or value depending on organisation’s focus) which is very healthy. In short, ML and AI will typically reduce the time of sales representative by 5-8 minutes and improve intelligence yield the desired growth.
  2. Sales Route Optimisation: Just imagine easily google map guides us the best route to take while we travel (saving time or saving distance or as appropriate). How about using the same concepts to derive our sales routes to maximise sales, profits, travel time & distance make every day work more efficient( cost reduction or sales maximisation. Successful implementations have led to savings of at least 6-8% of total cost of sales and servicing.
  3. Sales Forecasting & Target Setting: This is one of the most widely used business use case of Machine Learning. Using historical sales data, geographic & demographic data and other factors like seasonality etc, organisations are forecasting the sales trend more accurately. This helps in complete logistics and right demand planning which is critical for both Top line & Bottom-line.
  4. Performance Shift: Building comparative performance metrics and creating a nudge to drive productivity shift is one of the areas AI champions. The beauty is the seamless implementation of possibilities like: Incentive &=and reward design on a stretched target arch, making sales team compete in a healthy manner to shift over all organisation performance etc are any managements dream. AI is a master at handling such tasks at ease if implemented in line with right strategy,
  5. Better Supervision & Sales Coaching: This is the most recent and most promising area in sales excellence. Using AI, it is easy to design the right supervision tool and ensure right coaching inputs are delivered. The beauty lies in personalised basis individual salesman’s strength and improvement areas and his on data. Imagine having the salesman score card which keeps on improving with regular “AI powered Supervisor tool making a future ready sales force. “A Sales Director’s dream which can be achieved by AI aided supervisor, of course a great people capability.( We always believe, the Salesman is the Hero and SFA is a selling tool to help him/her win the game)

The List is endless and with the speed of technology capability & movement, it is only widening the horizon between AI enabled Sales tool Solutions and average transactional tool.

The article has been penned down by Prakash Sreewastav,  CEO, WINIT Software 

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