Data doesn’t lie. But what truths are you looking for?
The team from Audience Group outline how marketers can utilise an evidence-based data plan to justify marketing and advertising spend to the C–suite.
Business leaders are looking to their organisations’ real-time data and advanced analytics for something tangible on which to base decisions about how to pivot or innovate, where to invest and how to find operational improvements so the business can survive. Should that expectation extend to the marketing side of the business?
What do you think your management team would say if you asked them that question?
It’s time for a fresh data plan for a ‘new normal’, to map out the way you are going to access, capture, create, test, analyse and learn from available data to measure and improve marketing and advertising performance and drive business value.
Unless you take an evidence-based approach, you run the risk of strategies and campaigns based on metrics that look good, but don’t truly link marketing efforts to business value.
Here are some considerations for your new data plan.
Back yourself (and your budget requests)
So far in 2020, many businesses have found themselves rapidly re-working their marketing strategies thanks to the cancellation of in-person events, street marketing efforts and field marketing activities like tradeshows and roadshows.
It’s hard to say whether in-person activations will be happening next year, and we’re willing to bet those that hold the purse strings are considering whether or not they represent the best bang for the buck in the long term.
What is your business doing with the marketing budgets previously allocated to these activities? There is still much marketing to be done, so the question is how will you re-allocate that budget and – indeed – will you retain and grow that budget for marketing purposes in the coming years?
Reducing marketing budgets is an easy way to drop margin to the bottom line. Invested wisely, your marketing budget should be returning more than 1 x in gross profit. If you can use your data to calculate this, then you can prove it is more profitable to invest the marketing budget rather than cut it.
To put it in terms that will resonate with your business leaders, your data plan should identify the analytics and evidence you are going to use to back yourself, your budget requests and strategic recommendations. It should explain how you are going to show the correlation between marketing activity and business objectives, the bottom line and shareholder value.
Avoid confirmation bias
The Marketing team at ‘Company A’ ran a month-long radio campaign in May. That month’s sales figures were higher month-over-month, and year-over-year. The company celebrated what they assumed to be a successful radio campaign, and now believe that radio works well – especially in May.
But is that where the sales growth came from? Maybe. It’s believable and for many, it’s enough. They’ll base budgetary decisions on it and build campaign tactics around it. But unless the original campaign was designed with metrics that showed the direct correlation, where is the proof? You might find out that it wasn’t the radio after all, or that it isn’t a campaign that will yield consistent results over time. This could be a lesson you learn after a number of unsuccessful radio campaigns and wasted budget.
What if your sales peaked in week two of May and then declined? Maybe the strong month was a halo effect from your TV campaign in April. Perhaps this halo was enhanced by radio. Perhaps your sales went up but your market share declined. The category was buoyant so you reaped the increased sales, but in effect, the radio campaign did nothing to drive sales.
Now that so much data is available to us, it’s time to be more critical when it comes to how campaigns are tracked and assessed, and how you interpret the data. The recommendation you make is going to spend future dollars. Our assertion is you can do it more efficiently and effectively by avoiding the practices that support confirmation bias and using what the data actually tells you.
Look for – and measure for – evidential proof to back up your recommendations, to show that you understand the correlation between what you are suggesting and the repeatable outcome the business should expect.
Measure whether you solved a problem – stop measuring the solution
Many people bring solutions to the table, try them out and measure the success or failure of the solution. Even if the data they track says that the solution ‘worked’, they’re not truly measuring ‘success’ because they are not measuring whether or not they solved the problem.
A financial institution identified this problem: it needed more loan customers. More loan applications will yield more loan customers, right? So they implemented a campaign to drive more loan applications, set an objective for the desired number of loan applications completed and counted completed loan applications. The first campaign hit that objective out of the park and they ran it again, driving even more loan applications. The campaign was a success!
Or was it? Only if the actual number of loan customers went up. Actually, that isn’t even accurate because there is a fundamental flaw that must be addressed. The problem statement was faulty in the first place. What the financial institution needs is more profitable loan customers.
If that is the problem statement, then marketing must ask, ‘What makes a loan customer a profitable loan customer?’ Unused loans and lines of credit are not as profitable as those that are used, paid back and re-drawn again and again over time.
This organisation was previously basing segmentation on whether a person was likely to complete a loan application, and it was looking for people most likely to take out a loan.
For optimised results, the marketing team should be looking for people who are likely to take out a loan and use the loan. Targeting that segment with a campaign to drive more loan applications will provide the most business value, tied to a critical business objective, i.e. more profitable loan customers.
Link marketing activity to a fundamental business objective
Here are two examples of defined business objectives that a brand can link its marketing activity to:
1. Profit Growth
If you are expected to run a marketing program that contributes to profit growth (and who isn’t?) – that business objective is your touchstone.
In an effort to contribute to profit growth, a consumer brand’s marketing team decided to create a strategy to generate more prospects. They implemented a social post strategy but identified a problem: it wasn’t getting enough Facebook likes. They went in search of creative ideas to generate likes, with the belief that more would yield greater prospects and new customers over time.
As above, they were measuring the solution.
Let’s go back to the touchstone. To truly link marketing activity back to the business objective of profit growth, the marketing team needs to spend less to get new prospects and customers over time. Their original solution to that was a social post strategy and they were measuring for the success or failure of that strategy (number of likes and conversions via Facebook), but that is actually more expensive then well-targeted programmatic banner advertising.
Our assertion is you need to ask the right questions tied to a fundamental business objective and find the data that will help you answer the questions with actionable insights on which you can build your strategy. Your strategy should be based on evidence and your measurements should be relevant to the over-arching business objective – not simply measuring the solution itself.
2. More efficient use of company resources
We helped a client analyse leads and applications coming through to a sales team for assessment and qualification. This showed that marketing can take a more strategic view on how it contributes to the efficient use of company resources.
This team had a finite number of people to process all the leads marketing was driving to the business. We analysed the geographic and demographic composition of the applications of those that were qualified vs those that went nowhere to identify segments that were tying up internal assessment resources but not converting to an application. The client could stop targeting them and drive more valuable leads to the sales team.
The more leads the better? No. Who you target is important, but it’s also important to identify who you should stop targeting. Your goal should not be more leads, but rather more good leads.
The proof is in the [right] data
Click throughs, number of emails collected, leads generated, etc. can seem like positive results – and the data can even be presented in a positive light to show that the campaign ‘worked’. Basing decisions, campaigns and measurement tools on arbitrary metrics, hunches, beliefs, or even past anecdotal experiences can allow confirmation bias to take over.
Marketers who want to prove their value to the business will ensure the data they are working with is statistically valid and that it is used to answer critical questions that will inform a marketing, media and advertising program that will address the company’s business objectives in a measurable, consistent and repeatable way.
James McDonald and Tom Evans are the co-founders of independent, fast-growing, full-service media agency Audience Group and Ron Ramaiya heads up Audience Analytics.