People – we’re a fickle lot aren’t we? Short on time and with even shorter attention spans, we are spoilt for choice when it comes to the plethora of channels and platforms we can engage with. For marketers and advertisers this is obviously troublesome. An increasingly crowded and competitive marketplace means reaching your audience and being heard is becoming increasingly more challenging. So, it makes sense that marketers keen to maximise real-time impact rank personalised ad delivery and audience building as leading priorities for 2022. However, in their efforts to be heard, businesses often leave too little room to focus on the core issues ultimately slowing them down.
While marketers know accurate and comprehensive insight is crucial to ensuring individual relevance, research shows almost four-fifths (77%) haven’t yet achieved a single unified view of their data. By skipping over such infrastructure fundamentals, they are setting themselves up for inefficiencies and errors that hamper their ability to act decisively and effectively. Or in other words: they’re trying to run before they can walk – and stumbling.
Big ambitions, flawed foundations
Much-cited findings that recent disruption has pushed digital development five years ahead mean marketers are operating in a more complex landscape than ever. Alongside catering to swelling numbers of online customers – with e-commerce growing by $2tr globally in the last three years – they now need to fuel engagement across an even broader range of channels, while adapting to shifting behaviours and consistently delivering tangible results.
Amid this pressure, many are looking for a hand from smart solutions that can help keep track of changing conditions and inform nimble decisions, especially advanced analytics. Between 2018 and 2020 alone, sector-wide use of artificial intelligence [AI] rocketed from 29% to 84%. This year, more than six in 10 (61%) marketers plan on improving their ability to move with, and even outpace, developing trends by increasing their use of predictive modelling.
Such ambitions, however, are frequently greater than the capacity of existing set-ups. Recent studies reveal 67% of those with predictive aspirations still build reports in spreadsheets and 38% continue to cite manual data wrangling as a major challenge. Added to the overall lack of holistic data visibility, it’s clear most marketing teams are far from ready to level up their data use with new tools. The question is: what’s leaving them stuck in the past?
Overlooking data practicalities
One likely cause for ongoing inefficiencies is limited emphasis on technical necessities; with the best illustration of that being a comparison between what marketers want from data platforms versus analysts. Although both agree on the most desirable feature – connecting data from every source – the remaining top-five list for marketers is heavily geared towards immediate usability. Specifically, self-service reporting, recommendations, and easy maintenance without IT assistance.
Switching to the analyst point of view, priorities are notably different. Despite appreciating the importance of fast time-to-value, analysts place much more emphasis on covering the right core mechanics. For instance, they prize the scope to send data to a centralised lake or warehouse and end-to-end systems; two vital components for making data easily accessible across teams and smoothing the path from initial data collection to activation.
While it’s natural for marketers to prioritise fast functionality, these differences highlight that lack of understanding about practical essentials is posing a significant progress blocker. So far, lingering reliance on cumbersome legacy workflows has left two-fifths (40%) of teams grappling with poor data clarity and difficulty proving ROI, while more than half (53%) face persistent errors in analytical output and 34% of CMOs don’t even trust their data.
The right kind of automation
None of this, however, is to say marketers at the start of their data maturity journey can’t benefit from intelligent tech, especially when it comes to automating data capture, integration, processing, and application. As well as reducing room for manual mistakes and enhancing accuracy, autonomous pipelines will free more time for teams to steer and refine activity. This is particularly true for solutions enabling them to keep a closer eye on performance over multiple channels via consolidated, real-time reporting.
There are already several examples of companies that have used streamlined management tools to get their data under firmer control and start extracting its full value. Health and life insurer Vitality, for instance, has been able to tie its services with up-to-date information about individual habits. By translating data from biometric screening, its network of gyms and grocery stories into a single source of truth, it can better understand each customer and offer rewards points for those who make healthier lifestyle choices.
Following increased investment in its website technology, high-street giant Next has also evolved into a serious e-commerce contender. Enhanced abilities to accurately measure the returns of its marketing efforts have allowed the retailer to precisely assess campaign impact and determine where it should invest for optimal returns; with the result that it is now planning to invest “as much as it possibly can” in digital marketing specifically.
In addition to showing what can be done with a solid data framework, these examples illustrate where marketing teams eager to tap predictive modelling should be. Only once they have baseline systems running successfully can they begin bolstering agility by using forward-looking analytics to spotlight where they can adjust efforts for higher performance.
As standing out gets tougher and the marketing mix becomes more convoluted, it’s easy to see why quick fixes have increasingly potent appeal. But the reality is that shiny new tools are not an instant passport to improved data maturity. To stand the best chance of achieving data-driven effectiveness, they must roll up their sleeves and get to work on walking through everyday data basics, then think about picking up the pace.