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We sure have come a long way since the days of mass marketing snail mail campaigns.
Today’s growth marketers lean heavily on technical tools and capabilities to deliver highly-targeted, fully optimized digital campaigns and track their results.
But as it looks, we’re just getting warmed up in terms of the potential of advanced capabilities.
The introduction of AI into martech is empowering growth marketers to supercharge their optimization efforts, enabling them to deliver ‘never before seen’ ROI by acquiring best quality users, implementing engaging user experience mechanisms, and more. Results show significant improvements in retention rates, minimization of churn, and optimal pricing structures.
And since technology has proven to be not only beneficial but also a must, marketers are under constant pressure to discover and master the latest tools and technologies to stay one step ahead of the competition.
Take User Acquisition (UA) for example. This ever-evolving marketing discipline uses technology to instantly create real-time, direct channels between brands (or the people behind the brands) and their target audiences, up to the individual user level.
According to HubSpot’s ‘Not Another State of Marketing Report’, Ad Placement and Audience Targeting have become the top optimization tactics leveraged by marketers to generate new leads.
UA is just the tip of the iceberg in terms of the new marketing capabilities empowered by the latest AI technologies. A few quick examples: Chase Bank uses AI to fine-tune the copy in their marketing creatives; Starbucks leverages machine learning-driven predictive analytics to personalize their marketing messages; and Sephora integrates AI-driven chatbots to engage customers on social media channels.
With such an abundance of new AI-driven martech products around, marketers are championing their adoption in boardrooms around the world.
But here’s where things get sticky: now you have to deal with R&D to implement the new technology within the marketing stack — and guess what? They don’t have the will or time for it.
Sounds familiar? It should. This state of affairs has made many a fresh-faced marketer turn sour and resent their more technically-oriented colleagues.
You’d think that both marketing and R&D teams should enjoy a harmonious cooperation as they have the same interests at heart: They both want to integrate innovative processes, deliver world-class products, delight their customers and do what they can to build a thriving business.
Only they don’t.
Unfortunately, the reality is far removed from this utopia. R&D personnel often experience marketing requests as unpleasant interruptions to their ‘real work’. They’re busy struggling to meet impossible deadlines and chipping away at endless ‘to do’ lists and simply don’t have time to deal with ‘fluff’ marketing work. This feels like “fluff” because it is not in their main KPIS and OKRs. Only the most advanced and far-thinking brands have the DNA to allocate dedicated R&D resources to the marketing team.
R&D and marketing teams often find themselves at odds, dealing with different priorities and goals. This friction is especially acute in product-first companies where many believe that a good product supported by a strong R&D team is all it takes to succeed (yeah, right…).
Recent research shows that CMOs are currently expected to achieve their original KPIs with a reduced budget and resources, despite the challenges posed by Covid-19.
However, budgets focused on martech and digital channels may be the exception to this trend. According to Gartner’s 2020 CMO Spend Survey, many marketing and growth leaders are confident that their budgets in these two arenas will actually increase in the coming year, as they are expected to fuel growth.
The report states that “Technology currently accounts for the largest proportion of marketing budgets (26.2%), compared to media (24.8%), in-house labor (24.5%) and agencies (23.7%). CMOs remain bullish about technology heading into the next 12 months: 68% expect their already significant outlays to increase.”
The bottom line is that as technology, and more specifically data science and ML increasingly become woven into the fabric of the growth marketing function, the reliance on R&D to implement new tech should only increase — as will the underlying friction between marketing and R&D.
As marketing increasingly becomes a technically-oriented and data-driven discipline, reliance on engineering will only grow. As such, martech solutions that require little to zero engineering resources will experience a dramatic rise in popularity and usage. In fact, this critical factor may become a major deal breaker when it comes to choosing which martech solution to adopt. Those solutions that are able to deliver real value without requiring significant engineering resources will win every time.
And for a good reason. Zero code solutions are considerably much more feasible, less expensive and less time consuming than going the traditional, R&D dependant route. Take for example solutions like pencil, Wistia, and Databox, just to name a few.
Now, as AI and predictive (user level) LTV modeling become the darlings of the martech world, numerous new data science solutions are arriving on the scene. Like us, Voyantis.
Many of these solutions, like previous new technologies, offer promises that can only be fulfilled by drawing heavily on R&D to turn the latest technology into end-to-end solutions that can be effectively wielded by marketers. That’s a no go for most growth marketers…
Therefore, my money’s on those AI companies (literally speaking :) ) that ‘get’ the marketing/R&D conflict and deploy end-to-end solutions that DO NOT require engineering support. Those will be the ones used by the majority of companies in need of the wares they peddle.
I’m not alone on this:
I do believe that only a fraction of D2C/ ecommerce / gaming / SaaS solutions (or the like) heavy-duty media buyers have access to the resources required in order to leverage internal LTV predictions.
Why? It’s hardly because the knowledge is missing. Marketers know the importance of historical data collections, and they know exactly what data lakes are, and how data science can create models based on data. No, it’s because marketers need VERY heavy engineering resources in order to gain insights out of their data. And who has access to enormous R&D resources??
This creates a situation in which the majority of (heavy) media buyers run extremely limited UA campaigns that aren’t at all optimized to target the metrics they’re really after — high LTV, profitability, or other long-term metrics that can be optimized only based on back-end predictions.
It was for all these reasons and more that Voyantis decided on building a Zero-code, end-to-end, marketer-friendly solution. We realized that such an offering would be much more attractive to LTV-focused growth marketers, as they wouldn’t need to secure ongoing engineering resources to implement and maintain it.
As technology becomes increasingly prevalent in data-driven and growth marketing’s day-to-day operations, the reliance on R&D to implement and maintain martech will rise. Unfortunately, this will only exacerbate the existing, underlying tension between marketing and R&D that stems from contrasting interests and short-term goals.
The advent of AI, data science, ML and predictive analytics is only making this issue more acute. Brands that want to thrive will need to solve this issue to remain competitive. I believe that the only solution to this conundrum is the development of no-code AI platforms that eliminate marketing’s reliance on R&D engineering resources.
