Our thoughts on the future of business
Decision-First Marketing – Adding strategy to your MarTech stack
Alan | October 18, 2018
- Marketing Analytics have exploded but are frequently de-coupled from Marketing Strategy
- To realize the value of strategic marketing requires a much broader set of criteria than just short-term ROI. A “Decision-First” approach that couples strategic intent with data-driven insight can align marketing strategy and execution
We’re drowning in data. If we understand what we need to make the decision . . . am I making the right choices as it relates to corporate or customer objectives? Can I act on this data and make the right decision?
– Christine Crandell, the future of the MarTech stack
We recently had an interesting experience with one of our large customers during implementation. Our sponsor was the CMO in a savvy, well respected, organization. This implementation centered around deciding how to allocate marketing promotions worth several hundred million dollars. This decision was made at a brand level “in the trenches” historically with little formal direction or criteria.
Our platform allows a decision maker to set the relative weighting of selected decision criteria and then produces an outcome based on optimizing against that weighting.
When the CMO saw the tool in action for the first time, the realization hit her: she now had the power to tie hundreds of individual decisions directly to their marketing strategy. She stopped short and asked two questions:
How do we know these are the right criteria?
Who decides what the right weights for the criteria are?
This was a turning point for the organization. Once the overwhelming flood of data was aggregated and analyzed around a specific strategic question, new possibilities for driving their marketing organization were suddenly possible. The allocation discussion turned from 11th hour “food fights” to agile, intelligent, adaptive strategic planning discussions – and the ripple effects have continued over the past year as we’ve continued to work with them to refine the model around this decision.
The rise of data-driven marketing and the explosion of marketing technology companies have brought dynamic, detailed analytics to granular customer engagement activity. But there is a critical gap in the MarTech stack: Without linking business strategy to marketing execution, companies risk creating a purely tactical allocation. The result is that even the most data driven marketing execs are missing out on a huge part of the value that could be leverage by their MarTech stack.
Modern media mix modeling, algorithmic attribution, and customer engagement optimization have made good use of huge datasets of transactional data to link marketing efforts to short-term revenue upside. But strategic marketers must think more broadly.
- It’s not just about sales lift – it’s a multi-criteria balancing act of short and long term goals as well as understanding full costs (e.g. product profitability)
- It’s more than ad spend – it’s mapping big-picture segment, geo, and product goals to execution
- It goes beyond historical data mining, and requires predictive scenario modeling mixed with trade-off optimization
Only a few organizations are beginning to successfully align their marketing data and customer data platforms. Far fewer bridge those with their critical strategic KPIs. In next-gen MarTech, the current drive to link AI/ML to marketing has been focused on extrapolating the rich attribution data to “marketing in the moment” and context-based engagement for dynamic adjustments (RTIM). But the effectiveness of that investment will be muted if your top-level strategic marketing spend isn’t allocated with the same level of data science support.
Having a mindset that focuses on strategic decision handling is critical to overcoming the current data and process problems that arise due to this gap in the MarTech landscape.
The Data Problem:
This isn’t unique to marketing — we’ve seen this across industries and functions. The rise of ubiquitous data collection and analysis has overwhelmed the ability of the enterprise to make better decisions. Why?
- We’ve got much data, not too little
- We spend too much time digging through reports, and too little time using it to act
- When we do link data to execution it tends to be transactional, not strategic
Issue: Analytics tend to be single-criteria, and retrospective. Tools on the market now are highly accurate at maximizing short term lift, as well as providing a clear dashboard that tells you what just happened in your business.
Result: The folks running the myriad marketing campaigns and splitting budget for those campaigns across AdWords, FB banner ads, etc. have the data they need. But when a marketing strategist is trying to craft a multi-quarter plan based aggregated trend forecasts, it requires a time consuming, one-off analysis by their team. It gets worse when the complexities of balancing sales lift with other long-term goals like brand awareness, NPS, new segment growth, attrition/customer life cycle analysis.
Issue: Data collection has outstripped our ability to uncover the underlying value. We are now actively capturing nearly every customer interaction, attributing every sale to individuals, and linking these transactions to proximal marketing efforts. Existing analytic tools focus very effectively on certain specific metrics, but they lack the capabilities to link more complex data interactions to low frequency, highly leveraged decisions.
Result: Marketing organizations are drowning in data, and often struggle to get sufficient support from internal analytics groups and IT to answer higher level questions. The front-line data needs are well attended, but the ability to up-level the salient information to strategic marketing executives is missing.
The Process Problem:
While data-driven efforts have transformed transactional decisions, strategic processes remain off-line, analog, and therefore slow and data-poor. Available technology solutions have outpaced our ability to drive management change up and down our organizations (see Martec’s Law).
Issue: Media Mix Modeling engines are black-box algorithms that provide very high accuracy analytics on the lower levels of marketing spend – after all the strategic allocation decisions have already been made. How often are organizations optimizing a mix when the campaign itself is not the highest value use of scare marketing resource?
Result: Since Media Mix Modeling is focused on optimizing a single-criteria, short-term sales lift, it fails to account for other strategic criteria. Not all revenue is created equal, revenue that grows a customer base, retains an existing customer, or helps to establish a new product category has dramatically more value than just incremental revenue. As such, it misses any ability to be able to prioritize certain segments or products, a critical enabler in building a strategic marketing capability.
Issue: As mentioned in an earlier SnapStrat post, management’s process for the planning of key initiatives and strategic direction is still mired in analog processes. Annual or ad-hoc planning, subjective guidance based on the loudest voice in the room, and a slow, painful exercise that requires the same type one-off Excel models and team-productivity-killing research efforts each time the cycle comes back around.
Result: While many aspects of marketing have leapt forward in data and analytical savvy, the realm of enterprise marketing strategy has been left behind. The processes are byzantine and cumbersome, so the tendency is to stick with the intuition-based status quo, even though you’re sitting on a gold mine of data if it could just be rolled into a sustainable, repeatable process.
The Way Up: Think like a marketer. Act like a strategist.
It is not about adding complexity to your MarTech stack — it is about allowing data supported marketing strategy from the top-down so that you can reap the full value of the stack you have in place today (and future investments).
The Decision-First view of data:
By clearly defining the strategic decision “architecture” you are trying to optimize first, you prevent a wild goose chase looking for any relevant data. When the objectives are clear and the key criteria are identified, you can focus on just the data and analyses that truly matter, dramatically improving agility, avoiding “analysis paralysis”, and, most importantly ensuring that the outcome of the analysis can create real strategic value.
- The executive team can guide the data analysis based on their most important objectives.
- Any metric that comes out of that can be tested against a data-supported, correlated key result.
- The data sets get richer and the predictions and optimizations get more precise year after year, allowing you to take advantage of machine learning loops to continue to improve.
- If built with flexible data models, the objectives and criteria, the decision objectives and criteria can flex with changes in business strategy or context, creating a truly agile decision process
The Decision-First process:
With the decision architecture defined, several things fall into place:
- All roles and functions that touch that decision are clear, allowing you to get the right stakeholders involved in the decision process
- Existing workflows can be mapped – and then the decision platform can be configured to map to how your team works best.
- With the process streamlined and collaborative, and with a data-rich, laser focused analysis supporting it, you have the ability to refresh the decision quickly and easily. Suddenly, the promise of agile decision-making is in reach for your marketing strategy.
How SnapStrat Enables The Future of MarTech:
Marketing will continue to evolve and move toward data ubiquity – but regardless of where MarTech heads, linking those efforts to the business strategy that underlies marketing efforts and the goals of the entire enterprise will always be a critical piece. We have built the SnapStrat platform, analytic toolkit, and service expertise to make those links strong, truly agile, and sustainable. That’s why we exist; that’s our company’s DNA. Putting this type of tool alongside your enterprise’s most important recurring marketing decisions will ensure you find the breakthroughs in marketing effectiveness quarter after quarter.