Boosting Incentive Strategies with AI Marketing to Maximize Revenue
We all know the feeling; we get an email from a brand we like, and all of a sudden there is 10% off. Sometimes we say “great, let’s check out what they offer”, or maybe we say “what the hell? Why did I pay full price for it last week”, or maybe we say “10%, hmmm… not sure, but if it was 20%, maybe”. We all feel incentives and discounts are a bit of a loose cannon, but we have no idea how loose it really is.
Incentives are a very powerful tool that can make a major impact on a business. They can drive conversation, retention, and also win-back campaigns, but at the same time can be devastating to profit margins or the brand value.
The Big Data evolution, together with the new technologies available in the market, have brought to the marketing industry a wide range of new capabilities, exposing marketers to a variety of different tools and solutions. These solutions, in turn, enable marketers to understand their consumers’ behavior better, and provide a improved customer experience to increase engagement and revenue where the key elements in the game are personalization and automation.
However, although nowadays the marketing industry is continuously moving towards 1-to-1 personalization, it turns out that, for marketers, performing individual incentive and voucher matching is still a guessing game of trying to balance engagement gains with margin hits.
Incentives: What? Why? When? How Much?
It’s clear that using incentives might result in higher revenue than no incentives at all, but the challenge of assigning the right incentive to the right consumer is a totally different story, one that is not so easy to overcome.
In order to try and understand the main challenges marketers are facing when trying to use incentives on a personal level, first let’s explore the main incentives that are currently in use.
Based on a recent survey done among Emarsys customers, the main incentive currently in use is % discount. Besides this incentive, there are many more types that are also in use, such as: free shipping, fixed amount discount, free gifting, 1+1, and so on.
When it comes to incentive personalization, marketers have to decide which incentive type to provide to each consumer, as well as the discount amount that will make enough impact. Clearly, due to the incredible number of customers, and the huge amount of data available for each of them, it becomes infeasible for marketers to make the above decision on a personal level. Even if they could, the implementation requires huge amounts of laborious work that can’t be done manually.
Therefore, marketers nowadays still struggle to define incentive strategies, and use a blanket approach for all their consumers, such as ‘10% off’, for the pure reason that even a one-size-fits-all incentive performs better than no incentive at all. While this logic may be true, it leads to missed revenue, as some consumers might not need an incentive at all to buy. Or, alternatively, a lesser discount might be enough for most customers to make a purchase. Therefore, questions that have yet to be sufficiently answered include:
- Do I need to send incentives to all my customers?
- How can I distribute my incentives wisely to maximize my revenue?
- What incentives should I use for each consumer?
AI Marketing is the Answer
In order to bring insights and predict what incentive to assign to each consumer, a data science solution should be applied. Data science-based solutions are commonly used in the marketing industry, providing the marketer lots of insights recommending what actions they should take, which segment to focus on, and more.
Imagine that you as a marketer will have a data science-based solution that will recommend which incentive to provide to each one of your customers. Do you think that such a solution will be good enough and useful?
The answer is NO! There is a major part missing in such solution that won’t let it scale, the execution part. Without automation, there is a severe limit to the amount of granularity that can be applied due to the enormous time overheads required to match users to the appropriate incentive on an individual basis. The best that can be expected is broad-stroke segmentation, which still misses a huge amount of potential revenue.
That’s exactly where AI marketing (AIM) comes into the picture by bridging the gap between data science and execution.
AIM technology understands each contact as an individual customer, and executes highly personalized and compelling campaigns at scale. The data-driven insights of AI marketing boost engagement capabilities while revolutionizing the marketer’s user experience, completely reshaping their approach. Upgrade personalization to beyond-human capabilities, and spend more time defining strategy and creating content.
To be effective for a marketer, AIM solutions should perform the following actions:
- Make decisions on what actions should be taken for each contact in a database.
- Execute the most relevant messages for each individual consumer at the most opportune moment.
- Continuously improve with the power of machine learning.
Having such an AI-based incentive recommendation solution for marketer’s incentive strategies can easily transform every campaign into a smart win-back program, expanding content personalization to include tailored discounts for each individual customer.
Incentive Strategies: Future Thoughts
Once we have an AIM solution deciding which incentive to assign to each customer, the next challenge is to determine how often we should send incentives.
Sending incentives too often might increase the revenue in the short term, but might cause negative effects in the long term. The impact of overusing incentives might be that customers will be habituated to using incentives, and won’t purchase unless they have one available. Such phenomena will obviously decrease the business revenue in the long term.
Imagine that every time you step into a shop you see that there is a sale on. Will you purchase once you step into the same shop when no sale is available?
In addition, the incentive strategy must also be aligned with the business positioning strategy, as it might also have a negative impact on the brand value, positioning its products as cheap rather than premium.
Artificial intelligence is set to dominate the marketing industry by the end of 2017. This means that no matter how futuristic-sounding artificial intelligence may seem, in the near future more and more new AIM solutions will appear in the market, providing marketers new innovative capabilities that will enable them to greatly boost their campaign performance, as well as ROI, and all of this will be achieved with essentially no extra effort on the marketer’s part.
Stay tuned for the next chapter to hear more about how Emarsys is breaking new ground with our AIM solutions. In the meantime, request a demo to see how your marketing team can begin revolutionizing your incentive strategy with our innovative incentive recommendation solution.