Capture, store and activate the right data
Hanna Shirkavand
Data & Analytics Lead
This is the fourth post in our series around, “Why your main goal should NOT be to build a unified customer journey“. By now, you’re aware of the new technological era involving cloud technology for marketing, privacy-safe guidelines for collecting and storing user data. This all boils down to the data at hand.
The single most efficient way to increase marketing effectiveness
To capture the data that will make a significant impact, stop from time to time and look at where the platforms responsible for the majority of your marketing budget are heading. Where is Google focusing most of their development? What was Facebook’s latest revelation? For the purpose of this post, I have consolidated some of these marketing giants’ remarkable shifts, in this context, for you:
To say that the progress in recent years has made your first-party data more important is understating it by 10 million. Nearly every piece that used to delimit good marketing from great is now automated, putting the pressure on what value you provide instead.
“In 2021, feeding the right data relevant to your core objective is the most efficient way to increase the effectiveness of your marketing efforts”
Analyse the reports provided by your agency or marketing team, and reflect on what you base your marketing efficiency on. There’s a perception that ROI is a good thing, without much appreciation of what that means. Most people talk as if profit and ROI are interchangeable. They’re not. ROI doesn’t reflect which campaign is most effectively reaching your objective, only the efficiency in doing so. Secondly, what value does ROI add, if measured on gross sales when your business targets are based on net profit?
In not contemplating our data foundation nor diminishing return curves, we’re prone to errors in our decision-making from lacking data that directly addresses the question at hand.
Don’t settle for a correlation if you want causation
At Precis, our clients’ core objectives are mainly to maximise the effectiveness of something, being brand consideration, profits or Lifetime Value (LTV). What you, as an executive or digital marketing manager must be hyper-aware of, is whether your marketing optimization technology is fed data relevant to the objectives you would like to reach.
The marketing giants have automated the manual processes that required lots of headcount years ago – but they won’t know what they don’t know and that is the core objective of your company. Most companies don’t invest resources in activating the business-critical values to optimize more accurately. Worse still, they may not even collect the data needed to do so.
So, we invariably see banks optimise for leads instead of issued loans and retailers optimise for revenue instead of net profit. This is a consequence of focusing on things that can be measured easily, not what’s important. This is not to say that a company trying to optimise for profitability (or predict LTV, for that matter) can’t solve a similar problem using the data they do have. But that would require even more expertise and resources.
If you’re not measuring the long-term, think again
Think back to when you launched your last campaign. You may have measured the outcome during, then immediately after, the campaign was launched. This is a flawed approach; marketing effects can last far longer.
With access to customer data, the measurement of long-term outcomes is arguably more accurate. It allows us to see what a particular customer has bought and when, and how much revenue or profit resulted over different time periods. This is what we consider the LTV of that customer.
LTV is not a universal formula or tactic; it’s calculated and activated in many different ways. In some companies, it’s used as a key performance indicator towards investors. In others, it’s heavily integrated with their bidding or reporting. For some, it remains an uninvestigated path.
Our recommended approach is to use a machine learning model to compute the relative difference between customers, while the opposite end of this spectrum is that you’d start by setting a rule for how different your customers are. What happens when a human sets these relative differences? It might enhance the comprehensibility, but it’s most likely averaged, missing hidden dimensions in the data and static. Customer behaviour can shift from one day to another, teaching all of us the gains of responding quickly to shifts.
A common assumption is that the bulk of a predictive analytics project is building the model. In reality, at Precis we typically spend 60-70% of our time on understanding, cleaning, preparing, and validating the data into a useful and efficient format. This is known as Extract, Transform, and Load (ETL) processes, and when spent time on, the modelling process runs more smoothly and produces better results.
The final step of any LTV model is putting it to good use. Its sole purpose is not only to report how sustainable your business is. At Precis, we’ve built a scheduler for daily calculations of our models that are then exported to the main marketing platforms through pre-configured data pipelines.
This enables us to inform our clients’ BI or marketing teams of what marketing campaigns contribute to high-LTV customers, as well as target customers labeled “churn-risk” or “one-off” with the purpose of increasing LTV.
How can technology be used to reframe the problem?
The technology to collect and store data is declining in cost, and the tools to analyze that data are even cheaper (in many cases free), so what opportunities emerge from this?
What you can do with the data is the true differentiator. At Precis, we’re making new applications possible in the Cloud by marrying technology with people and business, then sharing it exclusively with our clients. Making use of immense computing power and integrated solutions for activation, a marketing data warehouse in the Cloud doesn’t just solve “old use cases” but comes with exciting opportunities to enable new ones too.
Conclusions
1. Infuse new thinking of what data and models your marketing KPIs are based on
- Project the long-term value of marketing
Regarding LTV: ask your agency or analytics team to analyze the different long-term multipliers and include that dimension in your marketing efforts.
- Get comfortable with using predictive models to fill your data gaps
Don’t get trapped in the crazy out there stuff, there are many applications where predictive models outstrip rule-based models, forecasting outcomes is one of them.
2. Data will never be perfect, but don’t be sloppy in your preparation
- Measure the cause and effect of what you want to achieve.
Instead of reporting what’s easily measured, integrate your business objective into ad and analytics platforms. In a future with less data, optimising on the right data becomes even more important.
- Don’t aggregate when you don’t have to
Your organization may be activating data, but if it’s aggregated to the point where it distorts the reality underneath, important nuances will be lost and you will lose out on potential.
3. Stop capturing data if you can’t take full advantage of it
- Traditional tools do not solve modern problems
Lots of data may be available, but a culture rooted in top-down decision making and traditional tools like weekly reports mean you can’t take full advantage of it.
- Ask (for consent), and you shall receive
Expectations from users are high, and so are the stakes for compliance. Ensure the data captured is handled responsibly and in a transparent manner.
Next steps
Now that we’ve fed our marketing platforms with a more valuable signal to our business, how should credit be divided between different channels and which customer interaction counts?
In our next and final blog post of this series our Senior Data Scientist, Pierre, explains the key ingredients to unlocking the power of a well-known marketing challenge: Attribution.
This is part of a five-part series on why you should not build a unified customer journey. Check out the other four parts of our series to learn more:
