Actionable data analytics: Multichannel marketing strategy optimization

Volume retailers have something in common. A huge amount of data in a couple of directions – customers and products. They are the key foundations of marketing strategy optimization when you have to launch a new product, bundle or a promotion.

Is there a way to maximize multichannel marketing efforts?

Actionable data analytics is doing exactly this. Through different data techniques and approaches, there is an opportunity to extract the maximum. Such optimization is not just possible, it’s a must.

From data science to great sales performance

First things first – creation of customers clusters. We assume that we have a lot of data which allows deep differentiation. Age, location, sex, time from last purchase, the amount spent, type of products purchased, response to previous promotions/new products, abandoned cars, etc. It can be everything and the best case scenario is to align this process both with marketing and data analytics experts. Sure, machine learning might be really helpful in a deep and precise segmentation of the existing and potential customers.

Notes from A4Everyone`s chief data scientist Alexander Efremov, PhD.

alexander-efremov-phd-data-scienceThe segmentation can be achieved by different approaches, depending on the specific task and the available data. For instance, if there is no information about customers’ reaction regarding particular promotions, which means that there is no dependent variable, unsupervised learning should be used to build the segments. Good examples of unsupervised methods, appropriate for segmentation, are the partitional or hierarchical clustering methods. On the other hand, if there is historical data from previous campaigns and the corresponding customers’ reaction is detected, then supervised learning methods should be applied like decision trees, different types of regression models, etc.

Customer journey and offering

Creating customer journey mapping is vital for delivering the right message to the right prospect at the right time. Multichannel marketing turns this part into a real trickery because it leads to a big amount of combinations that should be considered.

After defining the channels we`ll rely on our product/promotion launch we should create relevant offers based on our perception for the different customer segments, placed at a different stage of their customer journey via a different channel.

Optimization and data analytics

By having all three elements of our multichannel marketing strategy completed it is time to ask data analytics to aggregate actionable decision. This is the spot where linear programming comes to help. It is pure mathematics solving equations with few variables.

Like our goal is to maximize the profit of the launched product/promotion but we have general limitations that should be considered like budget, inventory, etc. Specific limitations driven by KPI are also applicable.

Notes from A4Everyone`s chief data scientist Alexander Efremov PhD.

From a math point of view, there is a set of unknown parameters, which represent the business decisions. An example of a decision is: “apply i-th offer to j-th segment”. Because of the two possible states: apply or not apply, the decision parameters are binary. This complicates the optimization problem, as it becomes an integer optimization problem. The goal is to determine the “best” decisions in terms of a preliminarily defined criterion. It could be maximum or minimum of an objective function, which is linear w.r.t. the decisions. Usually all business constraints are also linear functions of the decisions and for this reason, the problem belongs to the linear programming. The constraints can be offer-type or segment-type. Examples of offer-type constraints are: apply i-th offer at least to 20 000 customers or the budget related to an offer or set of offers should be less than 50 000 EUR. Segment-type constraints are: apply maximum two offers per segment, apply maximum one offer from a set of offers per segment, etc. The presence of both offer-type and segment-type constraints in the practical scenarios makes it impossible to decompose the overall optimization problem into subproblems. Frequently this leads to large-scale problems. For instance, if there are 50 segments and 1000 offers, then the linear integer optimization problem determines 50 000 decisions.

Sales forecasting? A step further in the strategy optimization

By knowing how much you are going to sell is easy to predict the right amount of stock you need to meet the customer demand. Sales forecasting is able to determine and evaluate different variables in order to create reliable predictions which are crucial in strategy planning in terms of resources like stock, budget, inventory, etc.

Sales forecasting based on historical performance data might be key element in every optimization strategy for retailers, coffee shops, restaurants, wholesalers, furniture stores and A4Everyone is able to offer such solution at affordable price. Visit A4Everyone` website for more information.

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  1. Pingback: How Banks Utilize Data Analytics. 8 Areas of Application | A4E Blog

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