How to tune data sources to the resounding epoch of your e-commerce business?/Part 4

Aktualizováno: 11. 5.

Data sources for e-shop management and planning

The main data parts that make up an e-shop business plan

  • Sales without VAT

  • Margin % without VAT

  • Operating (company) costs

  • Marketing Investment

Thanks to the decision to use Shoptet as a platform for your e-shop, obtaining basic data is very easy and available for you directly via administration

1. Sales excluding VAT and margin %: source is Shoptet administration

  • DO NOT use Google Analytics as a source of your total sales (of course, they also have their place in your data ecosystem, we will get to this later). Inaccuracies always arise in GA for various reasons, working here with returns (or importing other data into GA) is already a remnant of times gone (as well as saving local Excel files, as mentioned above). In 2020, even a small e-shop has to truly work with data at a real level if it wants to succeed in an extremely competitive e-commerce environment.

  • In addition, sales data can be easily exported from the Shoptet interface, thanks to which you can connect to other data sources and gradually obtain a more detailed and comprehensive picture of your Internet business. In my opinion, connecting data and searching for business opportunities is one of the keys to success in the e-commerce scene (Czech and global) in the long run. The second key is a unique service or product that has the potential to gain loyal customers with repeat orders, however, we will not address this topic in today's article).

2. Operating (company) costs

Unfortunately, your company's operating costs cannot be easily exported from any system. Simply because it's largely your unique business know-how. Personally, the option to keep these costs at least in the "high-level" segments already in the aforementioned Google Sheets template has proved most successful for me. Firstly because of collaboration with other colleagues, automatic cloud versioning of the document and last but not least because the data stored in Google Sheets can be further connected to other data sources and thus effectively puts the data into context.

3. Marketing Investment Volume

How much is required to invest in marketing? The easiest way to answer this question is to use a simple equation: "traffic x conversion rate x average order value x cost per visit." Are the resulting sales without VAT sufficient? Well, it depends on what business plan you have and what you want to achieve, see the chapter on KPIs.

These metrics are available for free in Google Analytics. Thanks to Shoptet, the implementation of Google Analytics includes an enhanced ecommerce section (will discuss EE next time in related articles), so just log in to your GA account, go to the most used Acquisitions -> Source / Medium report (see screen below) and you have easily accessible data.

Why are these business planning metrics missing the number of orders and the volume of revenue excluding VAT? They are not missing, only these metrics are already so-called "calculated metrics". This means that if you plan the volume of visits and the conversion rate % of your e -shop, you already have a planned volume of transactions (transaction = traffic x conversion rate %). Add to that the average value of an order by multiplying the number of transactions to get revenue volume.

Searching for connections in data or how to succeed in the field of e-commerce, which is dominated by Alza.cz in the Czech Republic and by Amazon abroad?

As I mentioned above, not even all the data in the world can compensate for the business "power" of the uniqueness of your product or service, the positive customer approach and the quality of work done well. At the same time, even the best product in the world will not sell if no one knows it exists. What marketing channels, platforms and technologies and people should you invest in? The answer to this question lies in the interconnection of data sources important for your business.

What data sources can, for example, support the growth of your e-shop?

-> Data from Google Analytics, where thanks to the well-thought-out architecture of queries to the GA API, we can also conjure things up from the standard (unpaid) GA for things that even the GA 360 does not have to be ashamed of

-> Data from your ad platforms, such as:

  • Google Ads (even data that are not available as part of GA integration, not even in the Google Ads API)

  • Facebook

  • Sklik

  • RTB platforms

  • Affiliate systems

  • Mailing systems

  • Manually entered data

  • And more..

-> Transaction database

  • Here, very valuable business information is stored not only about your orders and actual margins, but also about repeat purchases from customers (according to the hash of the e-mail or phone number).

-> SEO data of a technical nature, e.g., very detailed information from regular crawling of your and your competition's website.

-> SEO data of a business nature such as selected keyword positions, your market share as well as the competition and other information (one of the data sources for us in this case is Marketingminer.cz or Collabim.cz).

-> Google Search Console, which also contains very valuable information not only about the status of organic searches on Google.

-> Data about your competition, which is freely available and it is important for you to process this data regularly and use it for strategic decisions within your business.

-> And much more…

But how do you know your way around such a massive amount of information and when to find the time for it?

The solution in this case is to use more advanced methods of data processing and automation, thanks to which you "manually" go through only those parts of your data in which an opportunity worthy of your attention has been identified.

Unfortunately, advanced analytics and data handling are very often compared to the visualisation of Google Analytics data in Google Data studio dashboards. In better cases, at the very least using the basic data blend function (that is, merging data from multiple sources together). Unfortunately, there are a large number of "specialists" in the current market who only visualise data from one source in another visualisation tool (e.g. Google Analytics data visualised in Google Data Studio) without any further plan to work with the data, enrich it and actually use it for the development of your business. Unfortunately, at first glance, a layman does not recognise the difference between amateur and engineering data processing at the level of visualisation.

What is the difference between visualising data from Google Analytics that flows through a professionally created data infrastructure, compared to visualisations that are created with a native GA connector that is available directly in Google Data Studio? About the same as between a graphic designer who works in Adobe Creative Cloud and a graphic designer who works in Windows Paint :-).

A huge part of the work and know-how needed for professional data processing will be done for you by the Keboola.com platform, thanks to which you can process business-critical data from various sources to create a scalable, long-term sustainable data ecosystem (data model) that will enable you to compete with "small" e-shops as well as such giants as Alza.cz and Mall.cz for a fraction of the investment they make in the development of their data ecosystem. How is it possible? David also defeated Goliath, as his agility and sharpness were enough. You are small and in their eyes "below the distinctive level", but that does not mean that you can not be better for a fraction of the cost :-).

You can then use the data model built, for example, within Keboola.com for customer segmentation, automation of advertising campaigns both at the level of targeting and at the level of architecture for advertising accounts, to identify business opportunities and inefficient investments.

Does that sound too complex? Yes, in recent years e-commerce has become a very complex world, in which it is necessary to include a large amount of knowledge from several different fields. How can we achieve this? Collaborate with the right partners in areas where you do not feel strong yourself (eg. if you are not a web developer, set up an e-shop on Shoptet) and instead focus on those where you can build your unique position in the market.

Data visualisation tools

All the data that is currently important for your business is already connected in one place. But where to visualise the information obtained? Personally, I basically prefer to start with Google Data Studio.

Why use Google Data Studio?

  1. It's free!

  2. It enables very simple collaboration and sharing of reports and dashboards (for me, a key function for which GDS has no competition).

  3. Simple work with visualisations and their editing.

  4. A plethora of community templates (more here).

  5. It allows you to basically link data from multiple sources using the Data Blend feature (although this section may be more detrimental, as you read in the previous paragraph).

Other visualisation platforms and their advantages

  • PowerBI -> runs locally, thanks to which it has enough computing capacity to process a larger volume of data. It is possible to use for free.

  • Tableau -> very powerful tool for local processing of large data cubes (BigData), only paid (from approx. 20,000 CZK / year).

  • And more...

What views of data are absolutely essential for e-shop management?

Personally, I can no longer imagine managing an investment in an e-commerce project without these 4 key views of data:

1. A look at the cost and profitability at the level of the operating profit of individual marketing channels, including fixed operating costs (= order costs) as well as the cost of campaign managers, agencies, freelance consultants and more.

2. Customer analytics, thanks to which you will know the acquisition chart of your e-shop (i.e., how many new customers you acquire at a given time) and especially how much these new customers cost you (according to marketing channel) and also how much repeat orders cost you.

3. Attribution modelling according to a different methodology than the one offered by Google Analytics

4. Product analytics, thanks to which I know which categories and which specific products are currently sold, what differences are in the year-on-year comparison, etc.

Each of these topics would suffice for its own series of articles, maybe someday will be enclosed here or you can just get in touch with us to work together on your data-driven ecosystem. For a specific idea of what the visualisations of the above information may look like, I am enclosing screenshots from our platform for data management of e-shops Marketingintelligence.io.

13 zobrazení0 komentářů