Martech Stack Bloat — Does Your Martech Stack Need a Marie Kondo Clean-out?

Martech Stack Bloat — Does Your Martech Stack Need a Marie Kondo Clean-out?

No industry is immune to the wave of disruptive digital innovation that we are seeing today. At the core of this disruption—being driven by startups and incumbent players such as Amazon, Google, and Facebook — is the ability to harness data generated through customer transactions, engagements on many different channels, and, increasingly, connected smart devices. While business leaders recognize that they must move fast to become customer-data-driven enterprises, they’re nowhere close to the finish line. In fact, most are only starting to grasp the power of customer data to create customer behavioral profiles that can be used to drive profitable targeting, segmentation, cross-selling and upselling.

Most companies have been purchasing “point solutions” that they can implement quickly in hopes that the data insights these tools provide will help them catch up. Eager to offer customers personalized, valuable user experiences, these companies continue to grow their marketing stack; an understandable, but unfortunate mistake. Some research estimates that the average organization uses a staggering 91 different martech tools.

Bloated Martech Stacks Need Marie Kondo’s Help

The problem is that adding more point solutions is like trying to run at the speed of light: You’re trying to go faster, but the faster you get, the heavier you are, and the harder it is to accelerate.

Martech Stack Bloat is Spreading in Fortune 1,000 Companies

The fact that almost every company has followed this path this means marketers are all in good company, but a Forbes Insights survey revealed that leaders are now looking at ways of integrating the data to get deeper insights versus adding more point solutions that only add topical insights. In fact, only 13 percent of marketers were confident they were making the most of their data in their existing systems and processes.

The Martech Stack Bloat Test

If you’re worried your organization has fallen victim to martech stack bloat, consider the following question: Can you measure the value of the existing technology and prove its return on investment (ROI) for the purpose it was purchased for? If the answer is yes, you still might consider cutting any tools offering less than three times ROI. That’s a rule that I’ve found helpful and personally follow.

Calculating ROI in an Omnichannel Sales Environment is Tough

Unfortunately, there is no Marie Kondo for martech stacks. If your answer to the above question was no, then you first need to gather all relevant data on each technology, so you can measure the effect to calculate the ROI of the system. This might mean working cross-functionally with sales, customer support and account managers, which is time consuming.

Of course, whether a solution holds value or a significantly high ROI is not always a black-and-white a decision. For example, some technology is good for general awareness; this is where you can use things like multi-touch attribution to gauge impact on programs when ROI is more challenging to calculate, such as social media campaigns. But, again, if your data isn’t centralized, then this is impossible.

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Omnichannel Selling: Many Touches, Many Channels, Different Customer Journeys

Also, remember that in an omnichannel retail environment, there are often many touches, on different channels. One customer might click on a marketing link from an email, read a few web pages, download a coupon, go into a store, get info on the product by scanning a code on the tag, check out social media influencers’ views on the product, then order the product for pickup in a store.

Martech Stack Bloat Symptom #1

Here’s the problem you’ll have understanding each individual customer and customer journey. Your email platform knows about the email and the link, and maybe the web pages. But your email martech might not make the connection that the same customer scanned the QR code. And it probably won’t know where that scan took place, so it can’t associate the customer with geolocation data. The truth is, unless you’re using technology like a customer data platform (CDP), which ties together data from many sources and creates unified profiles of each customer, you’re probably suffering from a telltale symptom of martech stack bloat: FMS, or “Fragmented Martech Syndrome.” (Because, yes, we need more acronyms!) And we’re only talking about how tough it is to understand one customer journey, not thousands or millions, and at a realistic and profitable efficiency level.

Make Sure You Can Calculate ROI to Avoid Fragmented Martech Syndrome

If you are one of the lucky few that has not followed the pack in building a bloated martech stack that seems to grow every year, you might be wondering how to avoid falling prey to this cycle. If you find yourself considering an investment, your first step is to confirm you have a way to effectively measure its return. When you acquire any new tool or platform, you should test different strategies and measure which ones give you the best ROI. Without confirmation that the tool is working for you and driving results, you might find yourself seeking yet another new tool.

New tools and capabilities continue to come to market. At last count, there were more than 6,500 martech tools an organization could choose. As more marketers look to optimize their martech stack, we will see a move toward tools that break down martech silos, help marketers get the most out of their investments, and create insights faster. These insights fuel the innovation that will deliver the superior customer experiences needed to retain loyal customers, improve customer lifetime value and ROI, and delight new users and customers.

Erik Archer Smith
Erik Archer Smith
Erik Archer Smith is a data-driven marketing and sales professional at Treasure Data with 10+ years experience helping companies scale during phases of hyper-growth. Erik got involved with tech early and built the first social media site in Japan using open source technology in the early 2000s. When not working, he enjoys spending time at the beach with his wife and dog, and obsessing over character-build stats in whatever RPG currently has him hooked.
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