In my first post, I detailed how Intelligent Partner Discovery represents a shift in how brands can expand their stable of partnerships with maximum benefit and efficiency. By applying machine learning to historic and real-time partner program data, we’re helping brands identify their “best next” partners based on their propensity to drive strong incremental conversions and revenue.
This feature represents an evolution for partner marketing which, for the most part, still operates using very manual, human resource intensive processes that are left over from traditional affiliate networks. Just as with the other channels of digital marketing, over the next twelve to eighteen months, partner marketing will transform to fully leverage artificial intelligence (AI), machine learning, and data to provide automation and optimisation – freeing up those human resources to be reallocated to where they can add more value. Here are some ways this will happen:
Finding the best partnerships for your business
Brands will start to leverage machine learning-based tools like our Intelligent Partner Discovery to automate the process of finding the best partners to work with. This once manual process will expand to cover new partners such as influencers, who are now beginning to be tracked based on conversions rather than or in addition to engagement.
We will also see more automation in the tenancy buying process where brands have the ability to offer a tenancy budget to their partner base at scale. Partners can then bid on the budget and offer up placements. I wouldn’t expect this to end up as advanced as programmatic ad buying – but there are certainly options to use data science to automate what is still a manual process.
Going beyond the traditional partner types, I’d expect to see a data-driven approach used to connect brands with other brands. This will enable marketers and business development teams to be more certain of the potentially huge benefits of partnering with another brand.
Making sure brands are not paying out too much or too little
Partner commission strategies are often complex, especially with the amount of data available now. At Partnerize, there are brands that commission their partners based on multiple metadata parameters such as length of stay or loyalty tier in the travel vertical. But as complexity increases, creating the optimal commissioning strategy can become time-consuming.
Data science will help solve this problem by providing automated optimisation techniques similar to those used in A/B or multivariate testing. Partner marketers will be able to use these auto-optimising algorithms in conjunction with manual rules to make sure they are both optimising revenue and incentivising their partners. This will also apply to optimising commissions throughout the funnel, so that partners that contribute to the sale, but not necessarily the last click, can be rewarded fairly.
Beyond commissions, AI and machine learning will be more widely employed to help identify and block potentially fraudulent transactions, particularly in emerging markets or with influencer partnerships where fraud can be more prevalent. This task is perfectly suited to a machine – by analysing millions of transactions in real time, and applying the right machine learning, partner marketers will no longer have to worry that they are paying out for fake or inflated purchases.
Data, data, and more data
Partner marketers are only just starting to scratch the surface of how data can be used to provide insights and opportunities for optimisation. This is likely to be a key growth driver in the coming months as partner marketing programs mature. For example, partner marketers will be able to automatically segment their partners, just as they would with customers. Data will help them find over- and under-performing partners, and tune their strategies accordingly. This is going to be especially powerful for brands that engage hundreds or even thousands of partners.
Data will also be further leveraged to prove return on investment (ROI) and incrementality of the partner marketing channel. Expect to see automated dashboards that calculate not only the ROI for partnerships within the partner channel but also the contribution across channels.
Putting the customer first
As partner marketers embrace the techniques described above – many of which have been present in other channels of marketing and ad tech for years – we are going to see a shift towards a new customer-centric approach. With all other marketing channels, the customer is king, but partner marketing has lagged behind until now.
AI and machine learning can be the key to unlocking this customer-centric approach. For example, brands will be able to leverage real-time personalisation techniques to deliver messages and promotions on the partner site that are tuned to a segment or even an individual customer. Brands will also be able to dynamically update the landing page experience for a promotion based on that needs and preferences of the customer. These techniques can both optimise conversions and revenue for the brand, and also give the customer the experience they want.
It’s going to be exciting to see these and other ways AI will transform partner marketing over the next couple of years. The move to a true data-driven approach will undoubtedly grow the ability for partnerships to deliver incremental value to brands, partners and customers.