ca. 30 Minuten
nach Wunsch auch mit Videocall"How will my customers buy tomorrow?" - the answer to this question is not necessarily a look into a nebulous glass ball, but rather, it should be the result of solid analyses. When I know how my customers will buy tomorrow, I also know how much I need to invest in whom, and when, as well as via which channel, in order to maximize my profit.
The customer lifetime value is a customer-oriented key figure and an important basis when it comes to keeping valuable customers, increasing the sales of less active customers and improving the overall customer experience.
Companies should not only look at the purchase itself, but rather keep an eye on the entire customer journey: If I know which customer has bought via which channel, how often, when, what, at what price, or whether at all, and what his or her very special customer journey looked like, I am already able to segment my customers for certain purposes (activation for the first purchase, use of vouchers or special offers, etc.). However, due to the increasing complexity of the customer journey, it is becoming increasingly difficult to map and evaluate it comprehensively.
Hamburg-based AI startup and minubo partner Adtriba has taken on the challenge of shedding light on the complexity of customer journeys. The company has developed a dynamic attribution solution based on machine learning that can be seamlessly integrated with minubo.
Classic Attribution Lags Behind
Today, every marketing manager deals with the question of which marketing channels and measures are relevant and efficient during the customer journey and which can be neglected. Classic attribution models have so far supported the decision as to which budgets are used most effectively in which channel and how they interact with each other. There are various rule-based models such as Mutli-Touchpoint (position-based or linear) or Single-Touchpoints ("First Click" or "Last Click") that provide information about the value of a channel in the customer's conversion. Depending on the model, it is precisely defined how the remuneration is divided between the different points of contact.
An example from Affiliate Marketing illustrates the essential problems of the performance marketing industry, because here the attribution is decisive for whether, and in what amount, a commission is paid. If, for example, the entire conversion value is assigned to the “Last Click”, all other touchpoints are disregarded. It's no wonder that opponents of the “outdated” models scold them for being not up-to-date. They lead to significant undervaluation or overvaluation of the individual affiliates, because logically, the customer journey includes much more than just a click.
The challenge with these static models is to be able to comprehensively map and evaluate the increasing complexity of the customer journey to then make better decisions in terms of budget distribution and optimize the return on investment (ROI).
More Transparency Through Data-driven Attribution
At Adtriba, the weighting of the success of the individual customer touchpoints is no longer based on static models, but deep learning algorithms – that is, artificial intelligence – which are responsible for the data-driven attribution. The attributed order value is distributed dynamically and thus 'more realistically' across the individual channels. In the above example, this would accurately reflect the valuation of all marketing channels and thus enable performance-based compensation for the affiliates.
With their innovative attribution model, the founders János Moldvay (CEO) and Ludwig Ostrowski (CTO) want to help companies of all sizes to be more transparent about marketing performance and measure efficiency, in order to correctly assess and allocate budgets and optimize expenditure. The standardized SaaS solution integrates not only off-site events (e.g., clicking on a Google Adwords campaign), but also, on-site events (e.g. add to Basket, Product Detail View) and offline touchpoints (e.g. print, TV). All it takes is a few minutes to install the tracking code and a 2-4 week 'learning phase' to generate reliable insights from enough customer journeys.
Using Synergies
Mutual customers not only benefit from Atriba's attribution model and thus the most precise knowledge about the effectiveness of the marketing campaigns, but also from the use of the Business Intelligence Solution minubo, through which they can view their findings in detail at all levels.
The values of the individual touchpoints delivered via the standard interface from Adtriba can not only be displayed at the order level, but also, through the analysis in minubo, returned, canceled and adjusted up to the contribution margin level. The customer thus gets a holistic picture of the customer journey, including insights about the after-sales process, such as cancellations and returns, which happen after the purchase and would normally not be included in a “one-dimensional” evaluation. As a result, the customer can not only identify reasons for returns and minimize them, but they can also specifically adapt their product line, to optimize margins for example.
Furthermore, in addition to the transaction level, the dynamic attribution weights can also be distributed in minubo at the item level. This makes it possible to evaluate the ROI of a campaign at the product level and at the category level. Based on this knowledge, the customer can optimize marketing campaigns and use budgets more efficiently.
Through the interaction of the two solutions, the customer can not only understand the customer lifetime value better, but also increase marketing profitability sustainably.
