How #PredictiveAnalytics is Changing the Retail Industry | #CPG Take Note

This article is so important we are reprinting it in its entirety. As always, the link to the source is below.
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Ideally, a retailer’s customer data reflects the company’s success in reaching and nurturing its customers. Retailers built reports summarizing customer behavior using metrics such as conversion rate, average order value, recency of purchase and total amount spent in recent transactions. These measurements provided general insight into the behavioral tendencies of customers.

However, reports summarizing average behavior don’t provide the useful insights needed to determine how individual customers are likely to behave because general behavior tendencies are simply too broad. In order for retailers to create a meaningful dialogue with customers that honors the shopper’s preferred level and mode of engagement, it takes more than summarized reports, which is why customer intelligence and predictive analytics provide the opportunity to significantly change the retail marketing industry.

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. The process can best be described using the saying, “It’s not the data that is collected, it’s the data that is created.” Put into a predictive modeler’s perspective, the team not only collects a large amount of data, but also contextualizes that data by building derived attributes that provide additional insight into customer intent.

But how do data scientists and predictive modelers determine which derived attributes are relevant? Usually data scientists lack the deep domain expertise needed to clarify and prioritize their efforts. Therefore, a collaboration with domain experts is essential. This collaboration is like a three-legged stool. Each leg is critical to the stool remaining stable and fulfilling its intended purpose. When it comes to generating customer intelligence, the three legs of the stool are retail experts, data geeks and coders, and predictive modelers or data scientists.

Retail experts have domain expertise and can best frame the problem customer intelligence is aiming to solve. They suggest derived attributes that will provide value to both the brand and the company’s marketing campaign. Data geeks are needed to program these ideas and store them in a suitable database, which can often lead to greatly increased data storage requirements for the retailer. However, if the data can only be used to create solutions or make key marketing decisions if it’s properly stored and accessed. Inaccessible data means useless data and a wasted opportunity.

Predictive modelers and data scientists are then needed to use the stored data to build models that achieve those business objectives originally set by the retail expert. Predictive models find relationships between historic data and subsequent outcomes so that near-term and long-term customer behavior can be predicted. This leg of the stool aims to answer problems such as the likelihood of when a shopper will make their next purchase and what the value of that purchase will be. Sometimes, these relationships are so complex that only machine learning techniques will find them.

In a real world example, consider a retailer that would like to appropriately message high-valued, loyal shoppers who appear to be disengaging from the brand. A predictive model built from stored data could identify which shoppers are likely to purchase again with seven days, allowing the retailer to let them be the loyal customers they truly are. The predictive model can also show if certain shoppers are unlikely to purchase within seven days but have a high average order value. For these shoppers, the retailer could provide an incentive to bring the shoppers back to the brand. In either case, predicting what shoppers are likely to do is critical to understanding how best to complete the dialogue with them.

Moving forward, retailers will need to big data augment marketing decisions using insights gained from customer intelligence and predictive analytics. Each retailer’s data team must bring in elements from all aspects of the business, including retail experts, data geeks and predictive modelers. These key elements will set retailers up for success as we move forward into the era of big data.

Source: How Predictive Analytics is Changing the Retail Industry

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ZuumSocial Releases Facebook Leaderboard – Most Engaged #CPG Brands on FB

Last month Zuum announced it will begin monthly ranking of the 25 most engaging brands on Facebook.  The rankings are limited to the top brands operating in the U.S.  This is how they summarized their criteria for selection:

The goal of this is to capture the Facebook content and community traits of the top brands operating in the US. So we’ve put several constraints on which brands are selected. Brands must be operating in the US, we’ll use the US page unless there’s only a global page, brands must be consumer products vs B2B, and we’re excluding technology and entertainment brands, as our goal is to better understand how typical consumer products work on social media, and both of those categories tend to skew towards extremely high engagement due to the product type.

For July and August Monster Energy Drinks captured the top.  This month Zuum has released the rankings for a specific category CPG Dips and Dressings.  Here is the lead-in to their rankings:

CPG is a very broad business category, with sub-categories like soft drinks having brands with some of the larger social media fan counts anywhere. In our July ranking of the 25 most engaging brands on Facebook, the three top brands are CPG.

Of course, the CPG umbrella includes many smaller brands, with more niche or regional appeal. One subcategory in particular is dips and dressings. Below is the Facebook Leaderboard for some of the top dips and spreads brands in the US for the month of July.

You can see that the fan counts, while not what you’ll see from Pepsi or Coke, are still substantial. Posting volume is a little light in this category, with even the top brand, Sabra, only posting about 1 time per day.

Once the novelty of these new rankings are gone what value does this have to a CPG category manager or sales analyst?  “Well – Nancy . . .” – if your analytics cannot lay this new, unstructured data alongside your retailer supplied POS and inventory data – there is no value outside of water cooler talk.

This is one of the major limitations of rigid, outdated, DSRs built using data warehouse technology.  Rigid in the sense that to add new data feeds takes months – sometimes years.  The loading of the data – including current feeds – is long and cumbersome.  From a technological point-of-view these simply will break under the weight of increasing amount and type of data.

If you want to use these new – and exciting – data points (social media, weather, and et. al.) to draw insights and correlations to sales – then the only technology that can pull this off – today – is Big Data.

That’s what the buzz is about.  Will your brand be buzzing going forward?

We are Vortisieze.

Contact us today for a complimentary consultation on your BI strategy.

 

Sources:

Facebook July Leaderboard for CPG Dips and Dressings

http://blogs.imediaconnection.com/blog/2015/08/17/facebook-july-leaderboard-for-cpg-dips-and-dressings/

 

Ranking 25 of the most engaging brands on Facebook

http://zuumsocial.com/ranking-25-of-the-most-engaging-brands-on-facebook/

 

July Ranking of 25 Most Engaging Brands on Facebook

http://zuumsocial.com/july-ranking-of-25-most-engaging-brands-on-facebook/

 

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Top 3 Nuggets Buried In Your Retail Transaction Data

  1. What is selling – and where:  With POS data available daily waiting once a week to adjust store inventory is over.  Immediate shelf adjustments puts the right product in the right place.
  2. Customer buying patterns:  Patterns emerge from data about customer behavior and CPG companies can meet consumer needs before the consumer is aware of those needs.
  3. Is your data clean?:  Pulling in structured data into rigidly structured DSR data warehouses is fairly straight forward.  The retailer and CPG firm share and use the same definition for key retail elements.  But as CPG adopts big data technology that allows ad hoc, sometimes unstructured data to become part of the analytics pool, CPG may find that the category managers and home office are using different definitions for sale, customer, etc.  With flexibility comes responsibility – the responsibility of creating a single definition for common retail elements.  Big data allows CPG the flexibility to do this.

To summarize – many nuggets can be mined from the transactional data received from the retailer.  But it is increasingly critical that this data is brought in and analyzed much faster than before.  Big, rigid data warehouses bog down loading increasing volumes of data making it more difficult for CPG category managers, supply chain and manufacturing to respond quickly to changing consumer buying behavior.  Big data, by design, can reduce this process from many hours to mere minutes.

To understand how fast analytical insights can help you gain that competitive advantage for your brand contact us today for a complementary consultation.  (We won’t tell your IT director if you don’t.)

 

 

Source:  Consumer-packaged goods sector digs into transaction data

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Saturday Talk: How Independent #Retailers Can Leverage the #InternetOfThings to End the Checkout Line

Enjoying your Saturday morning cup of coffee?  Well here’s an interesting piece discussing how to use the Internet of Everything to enhance shopping.

See you back at the office next Tuesday.

 

Waiting in checkout lines is one of a consumer’s greatest frustrations when it comes to shopping. In fact, 52 percent of consumers report that they would actually take their business elsewhere if checkout wait times exceed five to 10 minutes. Independent retailers can end the frustration of waiting in line by using Internet of Everything (IoE) technologies such as mobile applications, Wi-Fi, sensors and predictive analytics to optimize the checkout or even eliminate it completely.

 

Check out the online article by clicking on the link below.

Source: How Independent Retailers Can Leverage the Internet of Everything to End the Checkout Line

 

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