Three ways in which Machine Learning can enhance Marketing Automation
The stakes in the marketing game have never been higher . Nearly every big brand out there works closely with intricate data in a bid to gain superior insights to target hyper segmented consumers. People are increasingly embracing personal digital wingmen to do the hard metric crunching that they can later optimize around. With the emphasis on huge volumes of data and lesser human involvement, the significance of employing machine learning techniques
has never been more apparent.
Picture this. You're a shopping app running a push notification or an email marketing campaign to promote your new range of women's fashion wear. Once the target demographic, say 18-40 year old women, have been identified, your archetypal campaign will serve Pushes or emails to this particular cohort. Not bad, right? I mean, certainly better than a 40 year old trucker going on his app to buy a work jacket and seeing ads for turquoise beads.
But this type of standard automation is routine to the point of stagnation. Procedural A/B testing certainly has its limits, mainly the inability to predict absolute, rather than relative, trends. The huge chunks of unstructured data that operation models have to typically work with, make rule based automation approaches look outdated. So, how exactly can we take this up a notch?
Enter ... machine learning.
By employing AI capable of constantly evolving and re-aligning its priorities with every user engagement or interactions with fresh data parameters, the possibilities of making intricately complex decisions in real time are endless.
Here is a broad look at how this can take marketing automation to the next level
- Predict churn accurately - Predicting, rather than reacting, to consumer churn is something businesses have always deemed a priority, and incorporating machine learning algorithm in marketing automation results in churn predictions with a far greater degree of accuracy. The fact that these techniques incorporate the added criteria of time in data segmentation and ability to handle new information at every turn implies a far stronger likelihood of foretelling a potential uninstall, which means you can approach customer retention models with greater sophistication. By eliminating the need for periodic assumptions, and identifying holistic trends (For instance, the correlation between time spent away from the app and likelihood of uninstall), a higher level of accuracy can also be achieved in implementation of risk and intervention models
- Personalize product placements - What if your campaign could be so finely tuned that you're not just eliminating consumers that do not fall under the broad segment you want to target, but also a sizable chunk of the desirable cohort as well, depending on their personal preferences and likelihood of viewing/reaction to a notification? In the marketing campaign example we looked at earlier, even a classic prototype of the intended target audience (say a 25 year old woman) will not be engaged with if she has shown no previous inclination to purchase or even browse similar products.
This also means that these eliminated users can be better targeted and engaged with by serving them Push or email recommendations more aligned to their tastes and preferences, again with the accuracy of these predictions driven up a notch by implementing machine learning techniques.
- Optimize user channels - Why blow a wad of cash up certain marketing channels when you can predetermine where returns can be maximized? By introducing machine learning techniques to digital marketing campaigns, you not only narrow down the people who will be served your ad, but also gain precise insight into what particular channel a consumer is most likely to engage with (in-app, Push, email etc). This makes complete monetary sense, but an equally big incentive is the fact that you won't have to clutter up the targeted person's interactive channels at every opportunity, resulting in a more aesthetic as well as a balanced advertising experience, which in turn increases the chance of user interaction.
So we can see how, by reducing the number of assumptions (and by extension the potential for human error) involved, machine learning techniques bring about a drastic increase in predictive power when compared to a standard statistical model, while working with much greater volumes of data. And, by implementing an algorithm that can constantly learn from data rather than a set of rigidly specified rules, a degree of personalization that is extremely finesse can be achieved.