MarketingCharts: Why Aren’t More Companies Connecting Big Data Dots for Loyalty Programs?
Through a special arrangement, presented here for discussion is a summary of a current article from MarketingCharts, a Watershed Publishing publication providing up-to-to-minute data and research to marketers.
While most marketers are collecting outside customer data for campaigns, many are not seamlessly integrating the data, causing them to miss out on audience insights that would better inform their loyalty efforts, according to a new report from Loyalty360 and Acxiom. For example, while 68 percent of survey respondents (the majority of whom are involved in audience measurement) gather outside customer data, 53 percent do not append or integrate third-party data into customer relationship management (CRM) efforts.
Additionally, 87 percent do not account for the cost of servicing customers when marketing to them, which leads to them not focusing on their most valuable customers. Roughly nine in 10 are also ignoring influencer scores and not using net promoter scores for segmentation and measurement. Combined, the results suggest that, while most respondents feel the need to engage customers based on their needs and expectations, they’re not leveraging the data in a way that maximizes the potential loyalty offered by customers.
The study cites various factors holding back increased customer understanding. These include lack of easy access to data (85 percent), an unfulfilled desire to locate and access all customer data in a single location (57 percent) and being limited to only using customer data available at hand to drive engagement (46 percent).
Another symptom of the larger problem emerges when examining social data. While more than 55 percent say they’re collecting this data, three-quarters have a hard time integrating and accessing social data to activate all media, 72 percent do not recognize the referral value, and 70 percent are not collecting social data regarding competing brands.
Failing to adopt a holistic understanding of the customer makes it difficult for marketers to cultivate better relationships, and this appears to be an ongoing issue. Last year, a study from RSR Research revealed that only 56 percent of retail respondents believed their company could identify their best shoppers.
The Loyalty360/Acxiom data is based on a survey of 130 marketing executives from a myriad of industries, conducted in late 2012. Nearly three-quarters of respondents were in audience analysis and measurement, with the remainder in broader marketing and sales roles.
Discussion Questions
What are the main hurdles to integrating and optimizing customer data across multiple sources? What are key steps retailers will have to take to better connect available customer data to marketing efforts?
Legacy mindsets and procedures keep many companies clinging to outmoded business practices. Another culprit is lack of know-how, in terms of using the new data. Finally, in some cases, gathering and integrating all of the data can be pricier than the practices they are currently using. While updating the analytics may pay huge dividends, these are not immediately obvious.
It is ALWAYS, ALWAYS, ALWAYS about the alignment!
The only purpose of data is to help align all the energies of an organization in order to reveal and realize new possibilities.
Companies spend a lot of money to obtain shopper insights and unfortunately, they are not being realized. Misaligned sales initiatives, the inability to process data quickly and cost-effectively, and not having a proven process to integrate the data across multiple sources leaves companies burdened and buried under the weight of their data and even more problematic, not knowing how to realize the ROI from it.
Companies that are taking a leadership position are using tools that are readily available to accurately and effectively organize data and integrate all of the critical and valuable analytic shopper insights to make this process actionable. Retailers, along with their manufacturer partners, have to make the decision to move forward with the process and solutions that will help them achieve positive results for both.
A close friend and data guru Scott Taylor has been sharing insights on Big Data and the heightened importance of Master Data Management (MDM).
Lessons learned:
As Scott likes to say, “Big data needs little data and little data is Master Data.”
The answer to effective data use is not a matter of collecting lots and lots of information; it is in aligning what the consumer needs with the organization’s own goals and then figuring out how the data can help you achieve both. This requires rather small steps: know what data you need, assure the message is relevant, and use the data responsibly. This last step may be most important for fostering loyalty—ascertain what you want to achieve with the customer data and then collect only what you need to do so. Once collected, use all of it in a way that benefits the consumer as much as the organization.
Liz hit the nail on the head. It is legacy mindset and more. The more marketing decisions are driven by the data the less personal opinion is a factor. “How can I market the idea I want when the data is telling me something different?”
They actually made a movie about this…it is call Moneyball. What Billy Beane came up against with his manager, coaches and scouts reflected exactly what happens in companies. Anyone who is interested in how people behave when challenged by new data should see this movie. It isn’t really about baseball. It is about a change in industry based on the availability of data.
Most retail operations are transaction driven and undervalue the importance the customer brings to the table in the form of repeat purchases, referrals, etc. The first step to having a holistic view of the customer will be understanding that treating the customer holistically will be valuable, then making the appropriate investment to combine the customer data together.
Unless brands prioritize combining and utilizing the data to drive customer marketing it will not happen.
The main obstacle to integrating customer data is that it makes little difference to the bottom line and in general, is a waste of resources. Connecting customers with retailers is not a data issue, it’s a service issue. Until a retailer commits themselves to providing customer service, no amount of data will make a lasting difference and if a retailer is dedicated to customer service, then the data isn’t needed.
“Big data” is being sold as a quick fix for inept retail management. It won’t work; money wasted on “big data” should be devoted to hiring and training.
Brick and mortar stores, which remain the largest sales effort in retail, are more closely and successfully governed by social economic demographics than any other form of leverage. The eradication of millions of life sustaining jobs in the USA over the past 5 – 6 years has brought forth an entirely new inventory and pricing line up in all but a very few of the retail stores “still” in existence. The practicality of using expensive company time and marketing dollars for rewards, and loyalty programs that are in no way capturing either market share or increases in customer spending in a vast majority of existing stores, are simply off the table for consideration.
Today, retail companies are dealing with double digit drops in traffic and sales. In the face of these facts, speculative plans for the future of retail’s e-commerce experimental expansion practices are not going to get the attention or time and money they need to succeed. What retailers want from e-commerce is a way to get more of today’s customers in their stores, in person or online, now. But that’s just what I think!
This is a somewhat amusing yet disturbing topic for me personally, having spent nearly six months of my consulting life working on this very issue within a large retail (supermarket) organization. As others have already accurately pointed out, the hurdles are many…and they are significant.
Perhaps the single greatest inhibitor to progress I experienced is a combination of cultural and managerial. Despite management’s loudly stated interest in profitability, the rest of the organization (in this particular retailer…and in most I have worked directly with) still lives by a “stack it high and watch it fly” mindset, first and foremost. Therefore, while a specific shopper marketing analysis I conducted (as an example) showed convincingly that it was both financially and strategically idiotic to match a competitor’s double-couponing effort in a market (it cost the company $2 million+ to do so and attracted the lowest priority shopper segment), the irrational, culturally-rigid adherence to ‘volume’ prevented this organization from making the clearly better business decision.
Yes, there are issues associated with data access, data organization, linkage with other data for insights, lack of resources, and so on. But at the end of the day, achieving loyalty/big data success is a management and cultural issue too many retail organizations are incapable and/or unwilling to hurdle.
One of the three greatest unfulfilled promises of loyalty marketing is the lack of use of the data collected through program operation.
The article and supporting research outlines the key considerations for realizing the potential of data to better execute data driven customer strategies.
One aspect of these findings is that few marketers measure their marketing spend ROI effectively. That, alone illustrates some of the obstacles to analyzing data inside and outside the retail or CPG enterprise.
There are tools available now from several vendors that take the gut feel out of marketing and can help create a true loyalty program for the merchant.
Well, I’ve gotta chuckle at the first sentence. Why are they not “seemlessly” integrating data? Because it’s not SEEMLESS! Integrating and harmonizing data to provide a holistic view of the environment is the most complex part of any “reporting” project. Reporting is the icing on the cake, providing true, reliable data in a format that’s easy to access is the hard part. It’s NOT seemless at all. I know because it’s why my company exists.
Think about the fact that data is coming in from sooo many different sources. Those sources all use support different file formats. They all use different data elements. They all have different hierarchies. They all have to be integrated with master data. UPC digits are never consistent. Files could be coming in EDI, txt, csv, SQL, etc, etc. But yet, that all need to be transformed into a DB2 or Oracle or Teradata or SQL Server. They have to be transformed into a common format while maintaining data integrity and aligning the data properly. Business rules need to be agreed upon, developed and applied. These are very high level issues we run into. Reporting vendors are NOT data integration experts. You need highly experience people to do this job.
Also, these are analytical applications, not operational applications. The data models are designed completely differently. Too often we see companies trying to have their ERP database administrators trying to develop analytical systems. That’s like asking a heart surgeon to perform brain surgery. The expertise is not the same.
Loyalty data is also massive data volumes. Unless a company invests in a massively parallel processing database, they will not be able to leverage loyalty data effectively because in order for loyalty data to have any meaning, it must be aligned with sales and events. Then it would have meaning. The costs for putting in an infrastructure to support this is often too expensive for most companies.
The main hurdle to integrating customer data across multiple sources is that it is HARD. The different databases are not designed to go together. As a result, the data splicing requires SQL skills to manipulate the data and organize it to be analyzed. In addition, data quality is usually suspect for one to all of the sources.
As a result, retailers need experienced analysts, who also understand marketing, to help build a single data set that can be analyzed and drive insights that lead to actions that lead to incremental revenue.
Big data usage also depends on your maturity curve. Would be useful to understand the maturity curve of the 130 respondents….
Having said that, I think the biggest deterrent is the mindset of complexity vs value of big data. I call that a mindset, since it’s more a lack of knowledge on how to manage big data and inability to understand its true value.