Uncovering Additional Insights With Custom Parameters
For years the affiliate industry has been premised on relatively standardised insight, usually defined by the suite of reports hardcoded into an affiliate network’s interface. But with continued and increased investment in the channel, so advertisers are demanding both greater visibility and flexibility to unlock precious insights from the affiliate activity on their programmes.
So over the years the use of data has become more sophisticated and lateral with an evolution in the number of data points (or parameters) that are tracked and reported on, through dynamic and adaptable business intelligence tools, allowing advertisers to make more informed decisions around their programme.
This could typically include a customer number to understand repeat purchase, more in depth product information or additional data points that are relevant to a sector; delivery details for a retail client or stay dates for travel advertisers for example. Bundled in real-time, this data can be used to create customisable dashboards, so the advertiser is both empowered and challenged to build more strategic relationships with their affiliates.
Custom parameter tracking has two core benefits. Primarily it allows advertisers to report on the metrics that are most important to them. It allows them to better understand their programme and use the rich data insights to further drive growth and to better target the most profitable customers. Additionally it also allows for more comprehensive benchmarking across a sector. The more advertisers passing us back custom parameter data within a sector, the better our ability to benchmark becomes and the more powerful the overall picture.
A number of advertisers have been passing back custom parameter data to us and it is now possible to gain additional insights into the quality of the customers that are being referred by each partner, building a fuller picture of the customer from initial interaction with a brand to their post-transaction contribution.
Advertiser A is a fashion brand. By passing back additional parameters they are better able to understand customer demographics and the type of customers that each of their publishers is driving. By also passing back product data in an easily digestible format, it is possible to see the products contained within each order as well as product details such as size and unit price.
The below charts give an indication of how it is possible to compare each publisher on the advertisers KPIs.
New and Existing Customers
By sharing this data layer alongside each transaction, it is easier to analyse the split of new and existing customers rather than having to match this back to commission groups that have been set up. It enables advertisers a quick view over the new and existing customer splits for each publisher.
For example, while Publisher A drives a higher volume of customers than Publisher B, Publisher B has a higher share of new customers.
Similarly, Publisher’s G and H delivered a similar volume of sales yet Publisher H was able to demonstrate a significantly higher share of new customers (78% versus 38%)
Average number of products
Additionally, by including the product layer within custom parameters, it is also possible to build up a picture of the sales that each publisher is driving, both in terms of the actual products and sizes, as well as the number of products within the basket. Combining this with the new and existing customer data, it is possible to cross reference these data points to see how the number of products vary depending on whether it is a new or returning customer.
Just as Publisher B demonstrated a higher share of new customers, these new customers also had a higher average basket size than the existing customers referred through the same publisher.
Only Publishers B and D saw new customers with larger baskets than existing customers. By using this data is possible to look at additional incentives to drive up the basket sizes of new customers – both in terms of the quantity and the value of items within the basket.
Finally, it has been possible to look at how the size of customers vary across two of the key publishers on the programme. Looking at menswear, Publisher B has a larger sized audience. By understanding the demographics of customers it is possible to further segment and target and build promotional plans for individual publishers.
As well as the examples above, it is also possible to combine a number of additional data points to paint a more in depth picture of how each publisher performs. For example, are new customers more likely to purchase when there is a free delivery offer? Are females spending more than their male counterparts? The more data points an advertiser is able to pass back, the greater the ability to analyse publisher performance.
One of the most valuable data points to pass back is a customer identifier such as a member ID or customer number. By sharing this it is possible to look at repeat purchase behaviour.
That is certainly the case for Advertiser B who operates within the takeaway sector. By reporting on customer number it is possible to see the frequency of purchase and how this varies across each partner. In addition to this they include details on the customer location as well as the cuisine for each transaction.
The chart below examines the repeat order frequency over the past month. While 75% of customers have placed a single order, 5% of customers have made four or more orders within that timeframe, (one customer ordered 22 times which made us wonder whether their oven had broken).
Again this can be combined with additional data points to understand the publishers that are driving repeat purchase, whether the customer is purchasing through the same publisher each time, and how frequently new customers are returning after their initial purchase.
By adding in the type of cuisine that is being purchased, it is possible to really understand purchasing behaviour and tailor offers for each publisher based on their audience and repeat purchase behaviour.
The image below considers the popularity of cuisines based on the top ten locations.
Finally, travel is a sector that has been at the forefront of custom parameters. With so much additional data tracked across the sector, it is possible gain further insights into the value of customers being referred through each partner. Think of the multiple products and services available and sheer combination of each that can add layers of complexity to travel purchases.
Booking to stay lag-time
For example, Advertiser C has been able to identify the booking to stay lag-time by each publisher and for each destination. This enables them to uncover the publishers that are effective for driving last minute bookings for each individual destination. They are also able to understand the number of nights stayed, the standard of the room and if the customer is a member of the hotel’s loyalty scheme.
The chart below demonstrates this on a publisher type basis.
When we start overlaying these data points with additional information that is captured as standard, we can really see how customer value varies depending on things such as the devices interacted with and the click to sales lag.
Custom parameters are a powerful tool that can unlock observations, previously hidden or misunderstood. The affiliate channel is often claimed to deliver a certain type of customer, often incorrectly, and they are a clear enabler for affiliate enlightenment.
Only by combining additional data alongside more standardised transaction data and then presenting it in an accessible, visual and empowering insight tool, is it possible to profile the publishers on any given programme and ultimately create truly rewarding partnerships.