With the retail market getting competitive by the day, there has never been anything more important than the ability for optimizing service business processes when trying to satisfy the expectations of customers. Channelizing and managing data with the aim of working in favour of the customer as well as generating profits is very significant for survival. For big retail players all over the world, data analytics is applied more these days at all stages of the retail process – taking track of popular products that are emerging, doing forecasts of sales and future demand via predictive simulation, optimizing placements of products and offers through heat-mapping of customers and many others. With this, identifying customers who would likely be interested in certain products depending on their past purchases, finding the most suitable way to handle them via targeted marketing strategies and then coming up with what to sell next is what data analytics deals with. We help retail and CPG clients with our expertise in machine learning in this domain with solving day to day and strategic challenges.
Of course, data analytics plays a very important role in price determination. Algorithms perform several functions like tracking demand, inventory levels and activities of competitors, and respond automatically to market challenges in real time, which make actions to be taken depending on insights, safe manner. Price optimization helps to determine when prices are to be dropped which is popularly known as ‘markdown optimization.’ Before analytics was used, retailers would just bring down prices after a buying season ends for a certain product line, when the demand is diminishing. Meanwhile, analytics shows that a gradual price reduction from when demand starts sagging would lead to increase in revenues.
Demand and inventory predictions
By evaluating and analysing seasonal, demographical, occasions led data and economic indicators retailers get a real understanding of customers buying trends, and the focus on areas that would have high demand. It also involves creating a good image of purchase behaviour across target market. Merging it with inventory management and predictions, retail business can maximise the profits.
This is also important in data analytics retail because choosing which customers would likely desire a certain product, data analytics is the best way to go about it. Because of this, most retailers rely so much on recommendation engine technology online, data gotten via transactional records and loyalty programs online and offline. Therefore, it means that when they get orders, they are able to fulfil them more efficiently and quickly while data gotten depicted how customers make contact with retailers is used for deciding which would be the best path in getting their attention on a certain product or promotion.
Almost 95% of shoppers have admitted that they use a coupon code when they do shopping. For retailers to gain from offers, they need to first ask themselves how valuable such deal would be to their business. Such promotional deals definitely will get customers rush in but might not be an effective strategy to sustain a long-term customer loyalty. Rather, retailers can run analysis on historical data and utilize it in predictive modeling for determining the impact such offers would have on a long-term basis. For instance, a team of data analysts and scientists can make a history of events that might have occurred if there was no discount. They then make a comparison of this with the real events when there were discounts to have a better understanding of the effectiveness of each discount. After getting this knowledge, the retailer will now readjust his discount strategy by increasing the number of discounts on various categories and removing less profitable deals. This would certainly boost the average monthly revenue.
Churn Rate Reduction
The creation of customer loyalty is the main priority among all brands because the cost of attracting a new customer is more than six times expensive than retaining the existing ones. It is possible to represent churn rate in various like percent of customers lost, the number of customers lost, percent of recurring value lost and value of recurring business lost. With the help of big data analytics, insights got like things customers are likely to churn, retailers can find it easy in determining the best way to alter their overall subscriptions to prevent such scenarios. For instance, a retailer takes an analysis of customer data after a monthly subscription box and can use it to get new subscribers who might likely end up as long term customers. This would result to the retailer decreasing the monthly churn significantly and would make brands be able to calculate lifetime value and make money back on marketing costs that are steep.