The Top 2 Retail Analytics Tools

The most successful retailers are data driven and fact-based decision makers. They harness vast amounts of data with retail analytics tools to better align products with market demand, improve consumer engagement, develop better customer relationships, and make better business decisions.

Most retailers have reasonable information reporting in the forms of real-time dashboards and historical reports. However, this information leaves them stuck in the past. The future of retail clearly looks forward, and the two retail analytics tools that most empower brands to proactively create their futures include predictive analytics and big data.

Predictive Analytics

There are many consumer interactions than can be improved with predictive models. When working with new clients I typically recommend beginning with predictive analytics to forecast offer responses, sales conversions and up-sell lift. Here's an example of how one offer-response model may work.

By tracking consumers offer-response behaviors to varying types of offers across various channels, the CRM system learns what types of offers consumers respond to and then categories consumers into offer-based segmentation – such as:

  • Offer-Induced—these are the persuadable consumers and the strategy here is to deliver highly relevant and personalized offers (generally for higher margin goods) in order to increase customer share and incremental sales.
  • Offer-Unnecessary—these consumers demonstrate repeat purchase patterns with or without offers so the retail strategy here is to avoid making offers for related products which consumers would purchase anyway. Instead only send offers for product categories from which they have not purchased.
  • Offer-Denied—these buyers only buy based on explicit needs. So, retailers should avoid sending them offers as this results in selling products at reduced margin that would have otherwise been sold at full price.
  • Offer-Adverse— these buyers don't like offers, and may respond negatively to being targeted. I often call these buyers the sleeping dogs. You should just leave them alone.

This buyer behavior recognition is one part of a predictive analytics model which increases offer conversions and margins by targeting receptive consumers and not offering discounts where they are not necessary.

Adding additional consumer intelligence delivers even more proactive strategy and forecasting accuracy. For example, including RFM analysis strengthens the model and gives retailers even more predictability.

CRM systems can automatically tabulate consumer purchases from the POS into RFM categories. This is often done as part of a loyalty application but a loyalty system is not required.

The RFM analysis displays consumer purchase patterns that when combined with additional data such as customer segment or information such as the prior Offer-Response behaviors further shows how retailers can identify the best promotional opportunities. Below is a simple RFM table which illustrates how to align campaigns based on consumer intelligence.

Recency
Frequency
Monetary
Action
<90 days
1-2
High
Nurture campaign promotions of higher margin goods.
91-180 days
3-12
Moderate
Increase promotions of complimentary or bundled goods.
>180 days
13+
Low
Life cycle nurture campaign promotions, including close out and higher discount goods.

The above actions are overtly simple for illustrative purposes. Each combination of RFM values will adjust campaign designs by promotion frequency, items and triggering events.

Expanding the table with additional consumer intelligence shows how that additional information improves the campaign strategy.

Recency
Frequency
Monetary
Offer Response
Action
<90 days
1-2
High
Offer Denied
The RFM analysis alone would have included this consumer in an automated campaign. However, also recognizing the consumers Offer-Response behavior changes that to exclude the consumer from the campaign.
91-180 days
3-12
Moderate
Offer Induced
In this table example, this consumer offers the most predictable upsell and margin lift. This consumer should be placed in a nurture campaign of higher margin up-sell, cross-sell and complimentary products.
>180 days
13+
Low
Offer Unnecessary
This consumer should be placed in a campaign that only offers products from categories which the consumer has not previously purchased.

Increasing the consumer intelligence with additional behaviors – such as purchase history by product category, customer satisfaction scores (using NPS or CSAT), the loyalty reward rate, the loyalty break rate or Customer Lifetime Value (CLV) to name only a few — will continue to improve customer segmentation, behavioral forecasting and campaign conversions.

Retailers have long struggled with the Right Product / Right Price / Right Channel / Right Time objective. IMHO, with the volume of products, fluid market conditions and fleeting consumer behaviors, attempting to achieve this all important objective in real-time without retail analytics tools is an uphill slog that will never realize the timeliness, accuracy and results of predictive models.

Big Data for Retail

Retail big data offers some big payback. The McKinsey Global Institute estimates that big data can grow profits in the retail industry by a whopping 60%.

Big data in retail is all about mining the volume, variety and velocity of consumer data streams in order to generate insights. These findings lead to quick recognition of shopping patterns and demand trends, more personalized marketing, more successful product launches, optimized assortment and merchandising, improved shopping experiences and better consumer relationships.

Or you can go further and leverage big data such as social media trending and customer sentiment to influence demand planning, determine price elasticity or recommend merchandising and inventory optimization by channel. The point here is that big data use cases are as varied as the consumer data itself.

Most retail executives recognize the transformational impact Big Data can bring to their businesses. But they sometimes struggle in identifying the use cases and supporting processes that can convert untapped data into improved decision making. Here are some examples that may stimulate that thinking.

  • Marketing Advancements
    Big data can be leveraged to develop micro customer segmentation, geo or proximity based marketing, real-time relevant offers, high propensity cross-sell recommendations and sentiment analysis by store, product, geography and channel. For example, sentiment analysis can inform retailers how consumers perceive their actions, offers and products—extremely valuable information for improving sales and marketing performance.

By cross-analyzing store and online interactions and conversions, and further cross-referencing the results by consumer demographical and geographical data, brands will discover with far greater accuracy how to pinpoint the ideal customers for select products, deliver messaging for improved engagement and create offers for improved conversions.

  • Merchandising Enhancements
    Harnessing data from social channels or in-store behaviors can improve product assortment, placement and pricing which results in smarter shopping experiences and positively influences purchase decisions. Uncovering patterns which may include pop culture events, online buzz or weather data can improve merchandise placement, bundling opportunities, promotions and pricing.

Going further, correlating buyer interactions across channels, such as in-store merchandising placement with e-commerce product categorization or placement can deliver far more empirical data to show how product placement, product bundles or product cross sell promotions are optimally positioned.

  • Supply Chain Optimization
    Applying big data to demand planning can aid just-in-time inventory distribution and improved logistics. It will help get the right products to the right destinations at the right times – and reduce both overstocks and stockouts.

For example, with improved market demand models which go beyond looking at seasonal fluctuations and historical purchase patterns and further consider fluid market conditions and real-time customer demand gathered from online and social channels, retailers can optimize product shipments of top-selling merchandise, reallocate inventory to locations incurring higher demand, know exactly when to mark up or mark down item prices (by channel and location) and get advanced notice of when product demand will recede.

  • Customer Experiences
    The analysis of what is being said by consumers online can provide retailers with valuable insights to enhance customer service and customer experience programs by store or across mobile and online channels.

Using a retail CRM system with social listening tools and integration to consumer social profiles can combine the consumer's demographics, firmographics and purchase history with far more revealing personal information gathered from social media, mobile app utilization, online and offline browsing patterns, and loyalty program interactions. The result is a far better understanding of the consumer’s persona and preferences along with what it takes to satisfy and delight the consumer.

In a recent retail big data project that I'm pretty proud of, we developed a big data model to perform product pricing elasticity by customer sentiment, segment, region, timeframe and competitors.

The client's consumer, inventory, pricing and POS data was spread across multiple systems and different formats. We used retail analytics tools to aggregate the data with the retail CRM system so that it could be appended with consumer social data. We used workflow rules and displayed results in dashboards, reports and a data warehouse. The data was revealing on many fronts. However, the biggest payback came when applying the price elasticity model with various consumer dimensions.

In one scenario, the pricing data was combined with a digital marketing campaign segmented by combinations of consumer profiles. The campaign yielded a 4.5% increase in conversion rate, and more importantly a 7.6% sales uplift with a 13% rise in gross margin.

We later used the data to improve our Next Best Offer (NBO) algorithm which has increased cross-sell conversions from high single digits to low double digits and continues to grow sales uplift.

For more big data examples that deliver big paybacks, refer to our prior post of 5 Retail Big Data Examples.