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How Progressive Retailers Are Getting Ahead With Retail Analytics

gaining the edge with retail analytics

Global retail sales are projected to reach $22.049 trillion in 2016. E-Commerce sales alone are projected to reach $1.915 trillion. It is no wonder the industry is prime for Big Data and Business Intelligence. Progressive retail organizations of all sizes are turning to Retail Analytics to gain the edge in a highly competitive industry.

The retail industry is not for the faint of heart. The landscape is constantly evolving. Competition is high and online and global marketplaces continually grow. Brick and mortar shops are decreasing as E-Commerce expands. The retail landscape now not only includes in-store customer interaction/transactions, but also managing websites, mobile sites, mobile apps and social media platforms. Highly informed, often fickle and more demanding customers are constantly challenging retailers. While the changing retail industry is hard for large corporations to manage, it is even harder for small- and medium-sized retail organizations. It is becoming increasingly difficult to be a small fish in a pond with giants like Amazon and Target.

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See 5 ways Retail Analytics help you to stay ahead of the competition!

Big Problems and Big Data

The retail industry has big challenges. Companies are desperate to answer those “up late at night” questions such as:

  1. Who are the next 1,000 customers we will lose? Why will we lose them?
  2. Which of our suppliers are at a high risk of going out of business?
  3. Which high-performing associates are we at risk of losing?

Luckily, retail also has data, a lot of data. While we all may be somewhat burnt out on the term “Big Data”, it really is the best way to define the massive amounts of structured and unstructured data the retail industry has amassed. The transactional nature of the industry coupled with email analytics, social media analytics, marketing campaigns, competition analyses, loyalty card data and even video analytics has generated a data surplus for the industry. Some of the big data examples expand into other industries as well, and it can be astonishing just how much data industries have collected on all of us.

retails data is everywhere

Retail’s Got 99 Problems, but Data Isn’t One of Them … or Is It?

Before we declare Big Data to the rescue (yet again), it is important to note the Retail Industry often struggles with harnessing and profiting from all this data. Retail data is often stored in disparate data sources; therefore, this makes analyzing the data across these various sources a challenge. Having both the staff and resources to perform these complex analyses is also challenging. Turning the data from noise into actionable useful insights that actually improve results, is yet an even bigger challenge. Retailers often miss the mark, sometimes ending up with fairly disastrous results.

Retail Analytics at Every Stage

Progressive retailers are gaining the competitive edge by effectively generating relevant insights and supporting business decisions for all key retail business processes. These processes include retail merchandise management, marketing, customer relations and operations. Let’s look into several of the ways retail organizations are, and aren’t, effectively using Big Data.

Insights-driven retail merchandising

Retail companies are using point-of sale, marketing, web-data, social-media and loyalty data to make informed decisions about pricing, promotions and assortment management.

One great example of how retailers are using this data is purchasing trends analysis. Retail analytics can predict purchasing trends using existing data. This is especially necessary for merchandise strategy before big purchasing cycles. There are various stages and methods to these analyses. Trend forecasting algorithms scour social media posts and web browsing habits to unearth buzz. Then ad-buying data is also collected and analyzed to see what the competition’s marketing departments will be pushing. On top of this, brands and marketers engage in “sentiment analysis” to determine the context when a product is discussed. All this retail data can be used to accurately predict what the top selling products in a category are likely to be.

Similarly, retail data can revolutionize demand forecasting and price optimization. All these functions are crucial to a retailer’s success.

Insights-Driven Retail Marketing

Having the merchandise customers want at the price they are willing to pay is imperative;  however, if consumers don’t know about your products, it is a moot point. Businesses are capitalizing on retail business intelligence to make marketing investments where they really matter. These business are seeing significant results. Grocery giant Kroger has been using business intelligence to generate analytically-targeted coupon campaigns. They have seen a 40% redemption rate from these data driven coupons, compared to an industry average of 2%. Kroger believes the promotions have increased overall sales by 5%.

marketing performance example dashboard

You don’t have to be Kroger-size for your marketing department to profit from retail data. Businesses of all sizes are effectively using business intelligence software to easily analyze marketing campaigns and their related Key Performance Indicators (KPIs).

With self service analytics, small to large retailers can profit from a Marketing Performance Dashboard like the one above. This dashboard asks the important question: How much are we spending and what had we planned for this campaign? The dashboard provides the perfect overview of the organization’s marketing performance data across its various marketing channels and campaigns.

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See 5 ways Retail Analytics help you to stay ahead of the competition!

Insights-Driven Retail Operations

Successful retail analytics can easily pinpoint improvement opportunities across the entire supply chain, from sourcing and purchasing to in-store availability management. At the simplest level, a KPI Dashboard for performance of your store associates can provide insights and subsequent savings for any retail operations. At a more complex level, data is collected from sensors throughout every stage of supply chain. For example, motion and light sensors may reveal areas of theft, sensors that detect shock may reveal areas of damage and sensors may help reveal whether or not food and drugs remained within safe temperatures.

This data can be used to react to current issues and proactively and strategically prepare for potential product and customer issues.

Personalized Customer Experiences

personalization sign

Knowing your customer is crucial. Digital shopping provides businesses with an increasing potential customer base. It has also created a highly educated consumer that can compare all competitive products/services with a few clicks.

Focused marketing and customer interaction is key to keeping a loyal customer and generating new business as well. Retailers need to analyze who their current customers are, where their potential customers are and how to keep them all happy. Transaction and demographic data generated at point-of-sale, through marketing tactics and through loyalty programs provides a wealth of retail data prime for analysis.

One example, the data can be used to forecast demand for individual geographic areas. Armed with this knowledge, retailers can be proactive and able to fulfill orders more quickly and efficiently. This is a great way to keep your customers happy.

Returning to insights driven marketing and merchandising, data on how individual customers interact and make contact with retailers can then be used to decide which is the best way to grab their attention with a particular product or promotion.

Analyzing and tracking this data through dashboards and KPIs is a great way for retail organizations of any size to stay ahead of the pack.

Predicting the Future with Retail Analytics

Customer intelligence is the practice of determining and delivering data-driven insights into past and predicted future customer behavior. To be effective, customer intelligence must combine raw transactional and behavioral data to generate derived measures.”

predicting the future with analytics

Retail analytics also has the ability to provide organizations with a “crystal ball” with customer intelligence and predictive analytics. This growing field is significantly changing the retail marketing industry. Dean Abbott, Co-Founder and Chief Data Scientist of SmarterHQ, has laid out an example that highlights the power of predictive retail analytics. In this scenario a retailer wants to reach out to high-valued, loyal shoppers who seem to be separating from the brand. Using business intelligence software this retailer builds a predictive model built from their stored data. This model is able to identify which shoppers are likely to purchase again within 7 days. This allows the retailer to segment their customers by how loyal they truly are. The power of predictive analytics doesn’t stop there. The model can also determine if there are shoppers that may not purchase goods/services in the next 7 days but have a high average order value. For these valuable customers, the business provides an incentive to bring the shoppers back to the brand.

In either case, predicting what your customer’s behavior is critical to understanding how best to interact with them. Now, Imagine using your existing data to make these kinds of changes? This is some pretty powerful stuff.

Exclusive Bonus Content: 5 Tips For Your Personal Retail Strategy
See 5 ways Retail Analytics help you to stay ahead of the competition!

It Is All in the Numbers…

In the end, retail organizations need to maximize profits and customer satisfaction. Forward-thinking organizations are turning to retail analytics to reach these goals and drive success in an immensely competitive market. These companies are turning to innovative business intelligence software like datapine to harness, access, analyze and visualize their data.


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