affiliate marketing, data, industry voices, affiliate program management

The Data Challenge | Why Bigger Isn’t Necessarily Better

Words: Kevin Edwards, Founder and Director of the APMA

We are delighted to launch our NEW Contributor Series, kicking off with this insightful article from Kevin Edwards, Founder and Director of the APMA. This month, Kevin shares his thoughts on data – the lifeblood of the affiliate industry…

The Impact of Data 

If you work in the affiliate channel, you’re probably surrounded by data. It’s the lifeblood that courses through every affiliate program’s veins and the DNA of the industry. 

The three key functions of a standard affiliate program: tracking, reporting and payments, are premised on accurate data collection and apportionment. Do these well and the rest is decoration.

That same core principle holds today. However, in the interim, the channel has grown in sophistication. Programs have diversified, thousands of new businesses have flocked to it and conservative estimates suggest it is responsible for more than £20bn of the UK’s retail sales. Beyond the millions of sales, consider the billions of clicks and impressions and the countless individual rows of information that will be tracked – in the time it takes you to read this.

While nothing in affiliate marketing is infinite, making sense of these seemingly unlimited interactions that are happening between thousands of businesses daily, remains one of our biggest challenges.

Is Knowledge Power?

In our desire to get under the skin of affiliate programmes and understand their true value, we are also asking brands and publishers to pass back considerably more data points, layering our quantitative knowledge with more qualitative insights.

Let’s take the example of an online travel business that wants to know which of its affiliates are driving their optimal customers. They can track stay date, location, hotel rating, car hire and breakfast bookings. This data can be aggregated, filtered and apportioned to individuals or groups of affiliates providing deeper insights and, in the age of AI, theoretically generate recommendations for us.

But in striving to learn more, we risk becoming ensnared by the thing we thought would liberate us. 

I remember numerous, enthusiastic conversations with retail affiliate managers who were excited about the new boundaries that data collection and analysis would break, giving them the edge and forging new strategies. When presented with these new reports, however, they were unclear about how to create actions from it and assimilate it within their business as usual. Consequently, the reports fell out of favour until they were abandoned altogether. Back to the drawing board.

My perception was that the data – and therefore the reports – didn’t easily slot into the day-to-day running of programmes and therefore weren’t embedded within businesses. They were powerful datasets but not necessarily outcome-focused. Without this structural approach, the reports struggled to position themselves as something other than a ‘nice to have’. 

What Does This Mean for Affiliate Programs?

Fundamentally a company has to decide what it is trying to achieve with its affiliate program. For new entrants that will typically be, well, sales. But affiliate programs are always on. This means, over time as programs mature and change in nature, new goals will be defined: targeted customers, new segments, maybe a focus on retention.

Having a clear vision from the outset should lead to a full understanding of what data points can help deliver insights. The critical step is how those insights are then turned into actions. It’s one thing to know that a particular affiliate is great at producing high-value sales, but what steps need to be taken to encourage them to drive more of those customers? 

That’s where data platforms have to be interoperable and algorithmic. This is especially true for programs with hundreds, or indeed thousands, of active publishers. Having a quality score, for example, or recommendations that are baked into reporting systems based on company goals, will help affiliate managers make quicker and more informed decisions.

This will typically be manifested in how to pay optimal commissions. In other words, brands will be more willing to give higher rates to those publishers who are driving the right types of customers for them. Of course, there’s a counter to that and it could lead to difficult discussions with other affiliates who are offering less value. But, we should all be striving for better and highlighting what good looks like allows others to improve.

How Far Are We From Such a Vision? 

It feels to me that datasets are less fragmented now than they used to be. Having these ‘talk’ to each other should produce greater clarity and be high on the priority list of any tech provider. However, there is still a reticence within the affiliate industry to connect publisher, advertiser and network data together to form a single customer view.

There’s also data minimisation to consider. This is the concept, baked into GDPR, that companies should only collect the data they need to do their jobs. Simply throwing the kitchen sink at the data problem on the assumption that at some point someone might want to use it should be frowned on. This is a cultural shift that companies should have made by now.

We shouldn’t be collecting data just because we can and platforms enable it. Clarity of purpose combined with a laser focus on the data needed to achieve those aims should be the foundations of any affiliate program.

I’m encouraged by how reporting platforms are now adapting to produce more algorithmic outcomes based on these goals. And with machine learning applied to complex datasets perhaps we are starting to see the bridging of traditional reporting and the context it needs to drive different outcomes. 


Key Action Points:

  • Define exactly what it is that you are looking to achieve with your affiliate program. 
  • Redefine these goals, as and when necessary, as the program grows.
  • Ensure that data platforms are interoperable and algorithmic.
  • Remember to retain a clarity of purpose.


Listen to our recent podcast with Kevin, where he discusses the work of the APMA, industry challenges that are being solved, and the one word that we should all be thinking about as we head into 2024. Listen in here

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