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E-commerce Data Analysis: Data-driven Marketing and Buying Strategies6 min read

When making the leap from small business to structured reality, what qualifies an e-commerce as functional is knowing how to work using Data Analysis. Whether it’s analytics for marketing, or data reading for market and merchandise acquisition, everything has to contribute critically to brand growth. An MBK Fincom best practice analysis explains the concept of Data Analysis for e-commerce, and how to make the most of it for growth.

Data: the most valuable resource of this century.
The constant buying and selling of data, the adaptation of technology in order to capture as much of it as possible, and the demand for figures increasingly specialised in reading metrics, have made Data Analysis one of the central activities in any business that develops online. If we are talking about e-commerce, it goes without saying that the discourse becomes fundamental.

In the article that follows, we try to understand both what a correct Data Analysis consists of, and the figures who must implement it, and how to make the most of it to improve one’s performance as an e-commerce.

Data Analysis: overview of the concept

Any digital information from collection tools becomes a datum; it can be quantitative activity reports, audience demographics, or simple performance metrics. What we need to understand, as a first step, is what data we need to optimise our business.

If we are talking about online sales, there are some fundamental metrics that cannot be ignored; these allow us to analyse the behaviour of our audience, the performance of our digital product, and evaluate any type of consequent action. Furthermore, by using the KPIs set before each strategy, you can use them to measure your performance (and possibly take action to correct it).

Among the key metrics to consider in analysis (through basic tools such as Google Analytics) are:

  • audience: audience metrics suggest whether you are pushing your product towards your ideal target audience, or whether you are rather dispersing (targeting too broadly) or ignoring useful segments;
  • acquisition: through acquisition metrics you understand how users find you, and from (and with) what medium they start interacting with your product;
  • behaviour: to frame the path users take on your e-commerce, use this metric. The next step is to improve your overall usability to push the user to convert as easily as possible;
  • conversions: when do customers convert? How long do they take? How do they pay, from what device, by what means? By analysing conversions you can measure the impact of your marketing strategy, and work on it;
  • paid channel metrics: not all traffic is (unfortunately) organic. If you work with paid campaigns (display, social, etc.), the best way to assess ROI is to be able to analyse metrics related to these segments.
A proper Data Analysis includes basic and vanity metrics.
A proper Data Analysis includes basic and vanity metrics.

Focus: vanity metrics

These are basically all those metrics, tending to be related to the social world, that measure interactions with your platforms that are not necessarily productive.

We are therefore talking about likes, followers, comments, shares; they are not among the most effective tools for measuring performance (for some analysts they verge on uselessness). However, a control and analysis of some of these indicators help to improve the relationship with your audience, the sentiment relating to your brand, and thus to design a UX closer to the needs of your users (whom we want, of course, to turn into customers).

Why consider all this?

Simple: numbers do not lie, unless misinterpreted.

If you want to transform your e-commerce into a Data Driven reality, and therefore into a digital product that knows how to use data as fuel for the growth of its results, Data Analysis corresponds, in automotive parlance, to the course of a good driving school; it provides you with structures and means, knowledge and materials, but it is essentially up to you to grow and improve in practice with these resources.

Improving the efficiency of your own platform, customising it by keeping it in line with trends, competitors and user demands are only some of the benefits; increasing sales of your own articles, through targeted follow-up policies and with a good CRM are among the many improvements you can draw from a good data analysis.

The Results

To give a practical example of what the impact is, we put two questions to Gabriele, SEO Specialist and ADV at ProduceShop.

Paid campaigns and SEO are basically two faces of the same coin, both aiming to gain visibility on search engines: how does data analysis intervene in this area?

As far as PPC campaigns are concerned, analysing data in a systematic way allows us to make all the necessary improvements so that the ROAS is within our target range, established beforehand taking into account factors such as marginality, seasonality and desirability of the product. Having full control of the budget and constantly monitoring the performance of each ad group is crucial for us. On the SEO side, on the other hand, data analysis helps us to identify potential problems (e.g. pages receiving little organic traffic) and subsequently resolve them with targeted interventions, carried out after a careful study of the reference SERPs and the main competitors.

Data analysis and identification of a potential audience, could you tell us more?

Never before has it been so important to know to whom your communication and product are directed. Interests, purchasing habits, demographic and geographic data are just some aspects to be considered in order to profile the buyer personas for a business. Starting from the data in our possession, the processing and analysis of a huge amount of information allow us to refine and make more and more precise the identikit of the potential customer, to be reached with an integrated strategy that brings together a series of different media. The primary objective of this targeting is relevance: delivering our offer to a potentially interested user means increasing the CTR, reducing acquisition costs, and minimising the dispersion of the budget, thus avoiding shooting in the heap in the hope that someone will choose us.

In conclusion

There is no strategy today that can stand apart from Data Analysis.

If one wants to set oneself up as a reality that pushes performance (and logically this is the most appropriate solution), a correct reading, interpretation, and subsequent implementation are the basis.

This, over the years, has led a business like ProduceShop to profile itself as data-driven e-commerce, with the results that, to date, can easily be assessed.

Sources:
  • Corporate PR
  • ProduceShop development and marketing departments (https://mbkfincom.com/)
  • Google
  • BigCommerce
  • Forbes
  • SalesForce
  • InsideMarketing
  • Metrilo
  • Research Gate
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