These levels of confidence are encouraging given the essential role data plays in enabling marketers to better understand performance and drive smarter decisions. But it also begs an important question — does perception match up with reality?
Most teams have a broad idea of what the gold standard for maturity is and 63% feel they are already achieving their goal of being fully data-driven. However, diving deeper into current capabilities indicates that not all have implemented the skills, practices and processes required to realise this ambition. In other words, many marketers overestimate how data or analytically mature they actually are.
So, how mature do you think you are? Or rather, how mature are you in reality? Here are five clear data personas to help you identify how far down the path you are to ultimate marketing data and analytics maturity.
1. Data dubious
You are likely to be found buried in a spreadsheet. Sceptical about the value of existing reporting, you feel it undermines team credibility and effectiveness, especially since marketing data is always full of errors. Your department spends time wading through data from multiple sources, leaving you with little visibility and proof of ROI. You prefer taking charge of processes, such as inputting data and going with your gut when it comes to making decisions. The Cadbury’s gorilla ad is your favourite example of how success can depend a lot on luck. You are least likely to say: “Data makes my job easier.”
You’re at the maturity start line and far from alone. According to our State of Play 2022 report, manual data wrangling is the leading challenge for 41% of marketing teams. In part, that’s because of the time it wastes. But of those citing manual wrangling as a problem, 53% have low trust in data due to mistakes and inconsistencies introduced by human error. It’s also no coincidence that with 58% of teams building reports in spreadsheets, the next major difficulties are measuring ROI (38%) and ensuring complete data oversight (36%).
Sticking with legacy processes not only burdens teams with hours of data trawling, but also slows down time-to-value from analysis and prevents companies from making the most out of their data.
2. Automation agnostic
You see some benefits of automation and have introduced it to streamline a few labour-intensive tasks, but use it minimally for data management. You view that as the realm of the dedicated analytics team, which deals with the mysteries of measurement and building reports. Although, their output doesn’t significantly influence your thinking anyway, as opinions and approaches are rarely aligned. With reports usually giving variable insights, you opt to focus on getting things done and telling stories without relying heavily on data.
Teams are a step along the scale, but retrospectively viewing campaign performance, often weeks or months after the event, means there is room for improvement. Too frequently, selective use of smart technologies means benefits don’t extend to crucial areas such as data input or communications, with 65% of teams continuing to transfer reports into PowerPoint and Google Slides. This increases the chance of discrepancies and delayed access which in turn amplifies the struggle of turning data into actionable insights.
Division between marketers and analysts makes matters worse, especially around data precision and business intelligence (BI) tools. Recent research shows 41% of analysts are worried about data accuracy compared to just 30% of marketers. This difference highlights the risk marketers face of basing decisions on data that analysts don’t trust. In addition to this, there seems to be a disagreement between marketers and analysts about which tools they have access to: 60% of analysts say they have BI tools while only 42% of marketers agree. To avoid misfiring campaigns, the two departments need to work closer together.
3. Integrated innovator
You operate from a unified control console. Using consolidated dashboards to track performance, you view data as an asset that is key for understanding and optimising marketing activity — which is how you’ve identified that omnichannel outperforms more traditional methods. Your campaign reporting is strong and you believe better insights mean better outcomes. You regularly engage with other teams to share insight and you are most likely to look for data platforms that combine intelligence in a single source of truth.
Part of the upper 54% of teams with end-to-end performance reports in place, those at this level are well equipped to measure, assess and fine-tune their activities using data. They are among those boasting good campaign reporting — and three times more likely to be strong on other strategic abilities, such as audience building and personalisation. What’s holding progress back is another disconnect with analysts, and this time, focused on tech.
While marketers want platforms providing speedy usability, analysts are looking for essential abilities to support everyday data activation such as sending information to a data lake or warehouse (72%). While on the right road, teams must find a way of balancing their ambitions and building basic mechanics.
4. Predictive pioneer
You consistently drive great results fuelled by diverse insights. Now real-time measurement is perfected, your next goal is leveraging forward-looking, proactive optimisation. Agile teams paired with smart technology is the ideal partnership, with your main objective to use recommendations from advanced systems as a navigational steering tool. Above all, you believe the best way to ensure success is through full organisational alignment, where all teams are able to collaborate and access the data they need in real-time.
The data house is nearly in order. Fuelling marketing engines on a stream of data, teams are eager to move away from retrospective insight and join the 61% aiming to use predictive modelling. But loose ends need tying up. For some, issues come from lingering reliance on clunky processes: 38% of those who plan on tapping into predictive analytics continue to grapple with manual data wrangling. Others may be homing in on the wrong areas to solidify data expertise, set on bringing in new specialist data talent, instead of re-skilling their current workforce and empowering employees to use intelligent tools.
Although 6 in 10 teams recognise the requirement to gain a unified view of marketing data, 57% of marketers and 41% of analysts are yet to do so. This suggests that tackling centralisation challenges is critical before they can drive sustainable predictive analytics.
5. Growth galvaniser
Your team plays an active role in company growth. Not only is every marketing decision fuelled by data-based insight, but you also provide intelligence that informs business decisions and identifies new growth opportunities and trends. Because of this, your organisation is shifting to a hybrid sales model that means reaching buyers at the right time, with the right message, in the right channels. In fact, your marketing approach is borderless: short, medium, and long-term strategies are plugged into wider organisational activity, with free-flowing data keeping everyone in sync and enabling nimble adjustment. You are most likely to say: “Data has become our biggest superpower.”
Effortless data-assisted marketing efficacy. A robust data infrastructure and interconnected pipelines allow slick, well-informed choices at every turn. Both marketers and analysts are in the 50% of leaders already using predictive analytics, in addition to including BI tech in their toolkit. This means teams can quantify their contribution to the bottom line and stand up to even the toughest c-suite scrutiny, something 35% of data and marketing professionals are unable to do.
But, guess what? Even though you have taken the steps from hindsight to insight and are well on your way to unlocking foresight, there is still further to go. Businesses need to foster a collaborative and supportive company culture where marketers are encouraged to experiment and given the freedom to fail. Once armed with the skills and the tools to create robust data practices and branch out to combine new interesting data sources, you’ll find yourself on the true pathway to ultimate data maturity.