Siloed data is still an issue for many retailers and that is a severe limitation when trying to integrate analytics data. Another big challenge is identifying customer behavior across social media platforms to truly understand buyer journeys.

A big challenge is that many people in the business unit of these organizations are not asking the correct questions that can be solved from the data that is available. Many people assume that the data is going to tell them what to do – and that is simply not the case. The data is there to help the marketing teams determine if their hypotheses and strategies are correct or incorrect, but the data isn’t going to do it for them. Data has the opportunity to be a game changer for retailers and brands in the future if the leaders can optimize it.

What I think is holding back marketers is not only siloed data, but also siloed organizations. In order to have cross channel analytics, departments need to work together, and given the legacy structures of many retailers, this may pose more of an organizational challenge.

Secondly, cross-platform analytics can provide a lot of value on customer behaviors and insights. The customer journey is cross-platform as well so there is a lot of contextual data at hand. The limitation is usually the actionability and creativity to utilize these data sets in a truly effective manner. Once organizations have found a workflow for this, I think it can become a big advantage.

Actually I’m surprised it’s as high as 43 percent. I think the real prize is using customer information to make merchandising decisions (not just offers). I am betting that number is even smaller.

It doesn’t surprise me that retailers and brands are slow at adapting cross-platform analytics. Historically neither category has been data driven in decision making. I have been in the business for 50 years and I have regularly seen companies spend money on research then make decisions completely contrary to that research because they thought they knew better.

In terms of consumer behavior, it isn’t that the data is poor. Facebook and Google sure know how to use it. I believe that there is a gut understanding that data takes decision making away from the decision makers. Shall I call it ego or ignorance?

But it will change. Those companies that embrace all data and its value will excel in the next five years, those that don’t will fade away.

This is all about the silos of data in my opinion. We have made investments in point solution analytics, products that come with an ERP system or a particular product, but nothing with a holistic approach that orchestrates the entire application portfolio. We need a unified commerce approach that eliminates the silos and marries the analytics across platforms. The only thing preventing this is investment dollars.

Data has become the same as standards. It was said that that global standards were a great thing – everyone should have them. The same can be said of data. We have access to more data than we can imagine yet very few organizations have the courage and organizational skills to ask, what is the problem that we actually want to solve? I worked on a project where a QSR had data to predictively optimize pricing, margin, and inventory on every menu item down to each specific location yet management just couldn’t find a way to execute across its organization despite the promise of increased profits and less waste.

In any company there needs to be foundational understanding about how data is stored, used and made available for business analytics. In other words, data governance. What has happened in the retail industry is that everyone from merchandising to marketing has built their own path, with their own sets of data and in some cases their own platforms to store and use their data. Cross-platform analytical tools are quickly becoming a requirement for retail organizations to try to bridge the gaps between legacy systems and data sets. This has to be done for the future success of analytics to support the needs of retail.

You can’t DO analytics without unifying and harmonizing data, let alone automate it using AI or ML. (At least with any integrity or trust in the answers). This “marketing executive only” skewed research shows the disparity between the front of the house (customer-facing) and the back of the house (operations that support those brands). The fact that these two entities still have not integrated their enterprise data shows that the gap is driven by the organizational structure and the disparity of competency around data-driven marketing as much as the tools. Doesn’t having up to date inventory or supply chain or pricing data affect CX? It’s more than just marketing data around the customer that matters. Until the front of the house is tied to the back of the house through an enterprise data strategy that comes to life using better-integrated data capabilities (there are best in class ways to accomplish this), this will always be a gap that needs to be fixed. Only then can you get to the level of analytics that provides the… Read more »

This problem has been solveable for years, and I remain shocked that organizations that call themselves “customer-centric” have not already enabled a single view of the customer. It is easy to find excuses, but customers and investors are running out of patience and will vote with their money on brands that get this right. Of equal importance is the customer insight you can get from complementary brands – that might be partners in a loyalty program. Most brands only know what a customer buys from them – but that data is hugely biased about the customer´s real lifestyle preferences. It is only when you know if they put premium gas in their cars once in a while, earn points far from home on a regular basis, buy one shirt for $200 or three shirts for $200 that you start to get a sense of who the customer really is and how they may respond to different types of offers. Brands must look beyond their own walls to really understand their customers – and the easiest… Read more »

There is a constant pendulum swing related to channels and insights – retailers invest to get one view of the customer, but then customer behavior shifts to new forms of interaction that are outside of the retailer’s current ability to track. Retailers shift their attention to attaining mastery of the new channels but focus less on that consolidated view – until the customer view gets so fractured that they have to make new investments in analytics to get that single view back.

This is a cycle with no end – retailers would do well to view one view of the customer or customer analytics not as a project but as a lifestyle.

Sounds to me like 57 percent of brands do not have a clear view of their own brand promise and core/target customer. Do they have product/customer focused teams or channel focused teams? The execution of serving the customer is certainly a lot more complicated than it was a couple of years ago. But that’s operations, whether it’s supply chain at the beginning of the process or fulfillment at the end of the process. The data should be driving the product that populates the process. It may take different listening skills to deal with the nuances of demand at the brick and mortar level versus the e-commerce level, but it all still has to come back to a focused brand promise and a focused core/target customer. Maybe pivot tables aren’t up to the job anymore, but it’s still surprising that only 43 percent of brands have risen to this challenge.

My concern is that retailers are constantly directed to spend vast sums of money integrating data in the hope that something will be discovered. What we rarely see are any examples of those efforts turning a sweet profit.

My recommendation to retailers: Before every additional tech investment, take a hard look at whether it has the likelihood of turning a profit. If not, there have to be extremely critical survival reasons to spend the money and usually there aren’t.

What is in the way of cross-platform analytic tools? Humans. Individual human mindsets. It’s hard to document or control the unspoken power of corporate fiefdoms, gatekeeper controls, fear of exposing knowledge gaps, etc. Much like the IT “expert” gatekeepers of the past, today crossing the cross-platform technical chasm intimidates many into submission. Why take the risk? This leads to the article’s question. Can cross-platform analytics resolve digital engagement? Likely never, given 20 years of trying, stymied by the interaction of a myriad of corporate, individual human behaviors. Yet as we all know, it can be done — as evidenced by the Amazon and Google corporate mindset.

This should not surprise use. Different parts of organizations have different needs and sometimes run at different speeds. Sometimes the “corporate standard” set of tools will not solve specific problems. The ability to consolidate single data views via real-time integration is a major barrier to attaining things like a single view of the customer in order to drive and enable customer experience improvements.

First, we need to recognize that this is hard stuff. First, getting all data in a unified data lake/pool/warehouse is very difficult. Second, ensuring data accuracy is difficult. Third, creating and deploying analytics that actually result in insight, let alone action, is difficult. Finally, getting the right stakeholders to decide and execute actions from those insights on a timely basis is difficult. Throw in the classic issues created by siloed organizations and, most importantly, siloed measurements and incentives and you have the perfect environment for inaction or late action.

Let’s get the measurements and incentives aligned and the source of those measurements centralized and then you can expect to make progress on enterprise analytics.

Taking liberties with an old adage, “Half the money I spend on marketing works; the trouble is I don’t know which half” is something that weighs heavily on marketers. The lack of comprehensive data feeds this challenge. The insights drawn from cross platform analytics can help marketers better understand digital behaviors, but it will take a multi-discipline effort to bring that to reality. In most cases, the data required is owned by marketing, sales, e-commerce, digital, IT and finance, so a broad initiative with the right executive sponsorship will be required to truly understand and improve customer experiences.

Good data is essential to a successful retail business. But consumers don’t make their retail choices using a single purchasing channel. To really know their customers and market to them effectively, retailers need the holistic consumer insights that only comprehensive, cross-platform analytics can provide.

But cross-platform analytics aren’t easy to implement. Apps, websites and online services accumulate data in application-specific silos. To access the bigger data picture, retailers have to make additional investments in data infrastructure and artificial intelligence. For many retailers, that additional investment seems like an unnecessary and potentially costly add-on to their marketing strategy, but they are worth the investment. Cross-platform analytics empower retailers to track consumer behavior across every channel and are an indispensable tool in crafting personalized user experiences that will drive the most sales.

Integration and the costs associated with it. A typical integration of even two simple cross-platform analytical tools will have several steps from governance to data cleansing to make sure the data is compatible and usable. These challenges increase the cost multifold and exponentially impact cross-platform analytics when attempting to leverage data for decision making.

The second reason would be accessibility to the right pieces of data. Most retailers are satisfied with partial data sets, but these don’t tell the full story — even with cross-analytic platforms and tools, data external to the organization is hidden making real time, accurate decisions challenging. Hence the rejection of leaders when their multi-million dollar investment doesn’t provide the silver bullet they were hoping for.

Instead, with smart evaluation and strong data science principles, organizations can leverage cross-analytic programs to offer up selective and focused solutions, limited data sets going after specific cross-analytic data that address specific problems rather than boiling the ocean. This is where the cross-analytic platforms can become valuable and earn their investment dollars.