Why Predictive Analytics Have Become Essential Technology For Retail Profit

Predictive analytics have quickly become an essential technology solution that retailers are eagerly adopting to compete and survive in today's marketplace. Predictive analytics solutions let retailers use science to analyze Big Data... using patterns found in historical and transactional data to uncover actionable insights and opportunities, to improve resource decisions, to enable the digital transformation of supply chains, to improve the digital shopping experience, to build analytics roadmaps to improve profitability... and more.

As retailers acknowledge and accept the importance and implications of breaking down big data into useful solutions, here's a brief overview of how and why retail got to this stage:

I.  Predictive Analytics help get more value out of data.

In an eMarketer survey, "Most Important Technologies for Achieving Value from Future Data Use", 49.3% of respondents (North American marketing and media executives) marked "predictive analytics and modeling" as the #1 technology for achieving value from future data use. See chart below:   


II.  Which marketing analytics need more attention than those currently being used?

In a global survey displayed in MarketingCharts, 561 senior B2B marketers and business leaders were asked: "Which marketing analytics need more attention vs. those currently being used?"

Predictive analytics ranked highest at 65%;

Web analysis, 50%;

Social analytics, 50%;

Business intelligence, 44%;

Mobile analytics, 44%;

Attribution tools, 44%.

Not surprisingly, the least important were "the need for spreadsheets" and "Structured Query Language databases." (Chart follows):


III. Reasons for developing predictive analytics capabilities.

"Goals for Developing Predictive Analytics Capabilities According to Client-Side Marketers Worldwide" ranked Increasing revenues, Improving customer engagement, More targeted and personalized communications, Reducing churn, Optimizing pricing to maximize profits, and reducing lapsing as priorities.  Chart follows:   

Chart Goals for dev pa capabilities emarketer


IV. What are obstacles to investing in predictive analytics technology?

Even as predictive analytics picked up steam, many companies continued to hold back, due to factors such as Siloed organization (32%); (lack of?) Internal knowledge or expertise (30%); Poor integration between systems (26%), etc. 


V.  Overcoming obstacles by using predictive analytics.  

"Predictive Analytics Proving Most Effective For Conversions, Most Difficult for Customer Insights." (MarketingCharts.com:  Study from eConsultancy and RedEye, July 2016).

* 59% of company respondents said, "the most critical skill for effective use of predictive analytics is understanding data in the context of the organization". 

* Disparate tech platforms and data sources are perceived to be the biggest barrier to using predictive analytics more effectively...

* A plurality (42%) of company marketers use a mixture of in-house solutions, third-party solutions, and outsourcing for predictive analytics.

* A majority (54%) of company marketers agree that they're yet to realize the benefits of predictive analytics.

* 2 out of 3 company and agency respondents predict an increase in budget for predictive analytics over the coming year.

* 61% of company respondents feel that their organization is significantly under-resourced for predictive analytics.

* Main focus over the next 12 months will be using data to provide strategic insight (23%); and getting internal buy-in (22%).

Bottom line for your bottom line:  

Predictive analytics allow organizations to become proactive and forward looking, anticipating outcomes and behaviors based on data, and not on hunches or assumptions. The ultimate goal is to get value using price optimization.

Serious data mining and text analytics, along with statistics, allow business users to create predictive intelligence by uncovering patterns and relationships in both the structured and unstructured data.

The best predictive analytics is achieved by, not only providing insights to suggest actions, but also provide decision options to benefit from the predictions and their implications.

Based on my front-line experiences developing predictive analytics in concert with clients' specific needs, I have to say that modern technology using predictive analytics works.       


For more information:

Key Elements of Price Optimization That Every Retailer Should Know (FREE download).

How Retailers Can Achieve Wealth Through Savvy Price and Assortment Optimization   (FREE download).  


Predictive Analytics Today





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Shai Geva, Co-Founder & Chief Technical Officer

About Author

Dr. Shai Geva is CTO and co-founder of Upstream Commerce. As a leading technologist with more than 25 years of practical experience coupled with a strong academic and research background, Shai is intimately familiar with web technologies and e-Commerce. Before co-founding Upstream Commerce, Shai served as Chief Scientist at Mercado (acquired by Omniture Inc., now Adobe), where he provided technology leadership, helping to formulate and realize the company’s strategy and vision. Prior to that, Shai served for five years in an elite technology unit of the Israel Defense Forces, where he was responsible for innovative system design and implementation. Shai holds a Ph.D. in Computer Science from Carnegie Mellon University and a B.Sc. in Mathematics and Computer Science from Tel Aviv University. He is co-inventor of two US patents and has a number of patent applications pending.
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