Retail And Data Science

Retail: Leveraging Data Science

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Retail industry is one of rapid growing industry today with large number of products making an entry into the market for consumers every day. Thus, consumers have multiple options available in the market for the products to choose from. Nowadays, with the growth of retail industry, competition in the retail market is getting tough day by day. The major challenges that most of the retail industry face today are:

For any business to start and to make sure it continuously grows, it very important to analyze the existing market trend to know exact what a customer may need today and near future. To get answers to this question, Data Science plays an important role. To start with, we will start gathering the historical as well as current data for the different products sales and consumption by the customers, different supplier’s product price details for the retailer’s, marketing strategy used and regions targeted by the competitors, competitor’s revenue details, seasonality impact on product and even customer sentiments about the products which may be available on social network.

Once we have all this data available, using Data Science we can process the data and then by doing Descriptive and Diagnostic analysis for the past and current market trend we can get the report generated that can have a useful information and insights like, which product or product type is mostly liked by the customers, type of suggestions made by the customers, competitor’s revenue for past years which can be further filtered to get revenue for individual category of the product that had maximum profit gain in competitor’s total revenue. This information, will help retailers:

  • Selecting the product category and product to sell
  • Selecting correct supplier and buying product from them at correct price and quantity at correct time
  • Selecting correct location for retail outlet
  • Offering the product with correct price to customer compared to competitor`s product price
  • Try applying the customer recommendations to the product if possible

Data Science even capable of predicting the future values based on existing patterns which can be done using Predictive analysis. So, retailers can use it to get

  • idea of estimate of their profitability in future, for different product category and product if they select it to sell and thus select correct product to sell
  • overall estimated profitability based on retail outlet location
  • profitability/loss fluctuations estimate based product price offered to customer

Making marketing strategy is one of the key aspects of any business to grow. Stronger is the marketing strategy, higher will be the chances of sales and thus profitability. Making effective marketing strategy mainly requires historical and current market trend knowledge and most importantly pitching the right customer at right time using the correct medium of communication which may be newsletter, promotional offers, personalized emails or sms, phone call etc.… Now, the tough part to this is, keeping updated with all current market trend and remembering or analyzing historical market trend manually. And it becomes very difficult if you are trying to analyze manually, not just 2-3 years of market trend data, but past 20 years of data. Also as this can be a time consuming and tedious process, there are high chances that accuracy may not be good as expected.

So, to make this process simple, easy, faster and accurate, Data Science comes into the picture. Doing Descriptive, Diagnostic and Predictive analysis, we can generate marketing trend report with a single click which can give a clear picture showing us market trend in a graphical view. This report can provide us detailed information like, impact of marketing strategy used by us or our competitors earlier, key factors that played important role in marketing, which region had maximum impact of marketing strategy, which marketing source had maximum or minimum impact overall and even region wise etc. These are just few of the features of the report. But more customized and more informative report can be generated with lesser effort that can help marketing team to create a marketing strategy using the correct marketing source and tools, targeting correct regions and customers and pitching the customers more effectively.

Supply chain management effectively is also one of the challenging part for any retailer. This comprises of multiple tasks like keeping a real-time eye on inventory and purchasing only required products with only required quantity to avoid over-stocking and out-of-stock situations from supplier at right price which can be done keeping an eye on fluctuating current market price for goods, transportation selection and management for good delivery to retail outlets on time.

This may seem a simple or somewhat manageable for small retail stores. But for bigger retail store, store manager may have tough time managing all this with keeping expense for all this in mind. To make easy, faster and reliable solution, we use data science which can not only make this task easy, but also optimize this task to improve supply chain management.

To create a solution to this, first we will gather the data related to buying details of goods by the customers, location wise details of customers visiting the store and historical as well as real time traffic information etc. Once we have all this data, we can do Descriptive and Diagnostic Analysis to know answers to the questions like:

  • What products customers purchase?
  • What quantity of product overall or individual customer purchase?
  • What combinations of products are purchased by the customers?
  • How frequent they visit out store to purchase product?
  • What is the product sales and consumption in different locations by the customers?
  • Which is the shortest and fastest way (having less traffic) to get the goods delivered to retail outlets from suppliers or even from outlet to customer’s location?

The answers to above questions can be found from the retail store invoice summary on which we can apply Descriptive and Diagnostic Analysis to get a graphical view that gives answers to all our above questions.

Further applying Predictive Analysis on this data retailers can get:

  • estimated product, product combination and even quantity that may be required by the customer and thus retailer may purchase it from supplier to make it available in store for customer to buy
  • an estimate by when customers may need this, retailers can try to get the products from the suppliers only when their customer may require so that they can supply the fresh products to the customers and thus avoid over-stock or out-of-stock conditions at retail outlets
  • estimate of customers residing in common location that may require the product on same day and time and thus retailers may offer the customers living in same location to send the product right away to their home or office charging them lesser or may be no cost for delivering the product creating win-win situation for both. As customers, will be happy to have the product delivered to their home at lesser or no cost and retailers will win confidence of customers, will be also able to sell the product immediately and get a chance to interact more with customers

Managing store activities plays an important role in a retail store to ensure proper functioning of the retail store activities. Store managers sometimes have a tough time to manage operational activities in their store. Some of the major problem they come across are:

  • Manage right amount of store staff at lowest cost possible
  • Preventing issues of shoplifting in their store
  • Customizing proper store layout for easy movement of customers inside the store.

To get solutions to all above problems, data related to historical customer visits, shoplifting details data, customer’s movement inside the store data can be gathered and by applying the Descriptive, Diagnostic and Predictive Analysis we can predictive information and recommendation that can help solving above problems.

If the store manager is presented with a graphical representation showing up the customer traffic at different time may be hourly in a day on seasonality, campaign dates, festive events and marriage periods, they can hire part-time and full time mix employees based this report. So, that they may have more employees at the peak hours or days and lesser staff to manage customers when it’s not a peak hour. Thus, hiring employees for only required period and paying them only for the time they work will help store manager to save cost that is being payed to store staff.
Similarly, store managers can prevent shoplifting by viewing the shoplifting pattern report. This report may contain type of product stolen, time patterns, product placement in store etc. Based on this report, store managers can take appropriate actions like, installing cameras at right places, training staff.

And finally store managers can view the customer’s movement inside the store traffic report and make appropriate layout changes to ensure easy movement of customers inside the store.
Engaging customers and retaining them is becoming one of the major challenge due to the continues launch of new products and increase in the number of competitors in the market. It’s becoming difficult for retailers to find ways to retain the customers keeping their profits in mind. To retain customers, retailers generally provide discounts and promotional offers to their customers and beside it some of them even try to personalize interactions with the customers to win their confidence. Although this both tricks work for the customer, but it becomes if you have a huge number of customers because it’s difficult to calculate discount for individual customers to be offered to keep the profit margin high. And, it can be a challenging task to have a personalized interaction with each individual customer.

To make this possible, we take the help of Data Science. Using the visitor buying information available from their invoice summary, using Descriptive and Diagnostic Analysis we can find out:

  • What kind and brand of product and in what combination they purchase?
  • How frequently they buy products?

Using this information, we can have the Predictive analysis further on same information retailers can get:

  • estimate of the discount offer that they can offer keeping their profit margins high
  • retailers can even offer another brand product that may be bit high in price, but customers may still purchase it just because it better suits their likings
  • retailers can display real time recommendations of products to buy on display while the customer is buying a product based on the combination pattern they purchase the product
  • retailers can automate the process of personalizing the interaction that is being done via email, sms etc. based on current buying pattern on previous communication that happened with a customer. So, let’s say for each an individual customer they can send personalized recommendation of products they may be looking to buy in near future or may be getting out-of-stock at their place.

Thus, we would like to recommend all retailers to use their data to help them grow at faster rate using Data Science. It will not only help them to increase their profit margins and make their life’s easy by having to focus on only predicted key factors and recommendations, but also retain and win more customers confidence.

Author: Prashant Gautam
             Data Scientist, VOLANSYS Technologies
       

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