The Complete Picture With Multi-Touch Attribution

The-Complete-Picture-With-Multi-Touch-Attribution

The strategy helps you identify customer touchpoints that lead to conversion so that you can reallot your budgets to maximise ROI

Though new channels and platforms are developed quite frequently to bolster marketing performance further, despite these advancements, it isn’t easy to understand how these channels and platforms work in unison.

Moreover, each platform may have a motive – that is, they want you to spend more money with them. For that, they can even withhold the complete picture of your marketing performance.

For a company, a complete image is necessary since every touchpoint a customer has with your brand can influence their decision to convert into patrons. A brand can use multi-touch attribution to comprehend the role played by each touchpoint in annexing new customers that subsequently contribute to the revenue.

Traditional attribution models often fail to accurately measure every touchpoint in the consumer journey, leading marketers to make bad decisions based on skewed data. Multi-touch attribution addresses this shortfall by eliminating biases algorithmically. These models will out each customer touchpoint’s value, leading to a conversion.

The models will allocate credit to every touchpoint across marketing and advertising channels (such as mail or paid search) and tactics. The final goal is to single out channels and campaigns that lead to conversion and redirect funds towards these points to acquire new customers.

A simple explanation of multi-touch attribution can be explained through a conversion event like the customer signing up for a free trial of any service – say, Netflix. The scrutiny of each customer touchpoint that leads to the customer agreeing to pay for the service will help a company understand its successful methods.

Traditional vs customised attributions models

Traditional attribution models include those that consider the first and the last touchpoints of the customers.

Many advertising and analytics platforms favour the last-touch attribution, which considers the last touchpoint of the customer journey for the end conversion. This linear attribution is easy to track. However, it does not elucidate the whole customer journey.

The downsides of last-touch attribution could be explained through the example of a Youtube pre-roll ad campaign and paid-search one running side by side. After perusing a Youtube ad, assume that some customers search for the product on Google. Or, they click on your paid search ad to buy the product advertised in the video ad.

A paid search advertising platform typically shows ads in response to queries. The best example of this phenomenon is Google Ads. The paid searches are touted to be great tools since it is intent-based. People see your ads because they are looking for a solution to their problems.

Last-touch attribution will tell you that people searching for your product will convert into revenue. But what about the YouTube ad that generated the interest?

Similar is the case with the first-touch attribution model that credits the first touchpoint of the customer journey for the end conversion. However, this linear model only elucidates one aspect of this complex customer acquisition process.

If we had to take the YouTube example again, the video ad would receive much credit in the first-touch attribution case.

Several models swerve away from the unilateral approach. For instance, the time-decay multi-touch attribution model considers the interactions closer to the conversion event. The U-shape multi-touch attribution model pays attention to first-and-last touch attributions; however, it has no focus on mid-level engagements.

The W-shape model takes care of pitfalls in all these models as it considers the first, middle, and last touchpoints. This model is best suited for complex cross-channel campaigns with many touchpoints.

If none of these models fit your company’s operations, you can create a custom model. This process is complicated and requires intense knowledge of the models that have previously worked for you. A custom model will allow you to distribute credit for conversions. They will, in turn, tell you which points are working well for you.

You could adjust features like:

  • Lookback window: This will decide how far back the attribution model will look to give credit to interactions with your ads.
  • Adjust credit by interaction type: Determine a multiple for the amount of credit to provide impressions as opposed to other interactions in the conversion path. A low decimal value can be set to give impressions less credit than other interactions.
  • Set up credit rules: Custom credit rules can help you choose how much credit to give interactions that match the rules. For instance, you can attribute added credit to interactions that perfectly match a paid search keyword.

Right time right channel: Leveraging AI to meet consumers

According to Forbes, 87 per cent of millennials use two to three tech devices at least once daily, which means there is optimum room for them to access various channels of a brand. It has created a huge opportunity for brands to present their ads to the right person at the right time and every time.

To leverage customers at every channel, data is required. However, many firms will help you out with their dedicated data teams and AI techniques if your brand does not have them. They will collect every interaction between a customer and the business to inform their decisions. You will get to know at what point the conversion takes place.

Automation companies like SaaSquatch, which has clients like Western Union, help achieve this goal. They create omnichannel incentive campaigns to increase customer lifetime value. SaaSquatch allows campaign marketers to improve every customer lifecycle stage from acquisition to revenue optimisation, referrals, and reviews to retention.

Automation companies can equip you with robust analytics and insights, highlighting growth programs that positively impact the bottom line revenue so that you can drive even more of it.

Conclusion

On the whole, multi-touch attribution delivers a full understanding of the customer journey, even if it is complex or fragmented. The results will help you adapt your strategy and optimise your ad spend to match the market shifts.

Furthermore, multi-touch attribution has become more accessible. You can start today with your customer data platform – by collecting all your data, funneling it into one place, and then sending it to data modelling and visualisation software. That should get you started.

Want to know more about multi-touch attribution, register for VMF South Africa 

If you liked reading this, you might like our other stories

Perform or Perish-Performance Marketing Trends
Data As A Default, Google Leaves Last-click Behind