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eCommerce Facebook Group![DataWow Gaining liquidity – identifying slow movers [Werbung]](https://ebakery.de/wp-content/uploads/2021/03/datawow-liquiditaet-gewinnen.jpg "DataWow Gaining liquidity – identifying slow movers [Werbung]")
DataWow Liquidity Gain
Identify slow movers and not movers with this analysis tool [Werbung]
In this DataWow Screencast Tutorial, we’ll take a look at how you can use DataWow – the online store analysis tool – to identify your slow movers and non-movers so that you can take measures to safeguard your liquidity.
Review of the last episode "ABC/XYZ Analysis
The last blog article about the online store analysis tool DataWow focused on the ABC/XYZ analysis. If you missed our previous videos and don’t know what to do with the term DataWow, you can find the link to our playlist again. The purpose of this episode is to identify the non- or slow movers, find measures to gain liquidity, and then follow up with a look at how things are going.
DataWow Gaining Liquidity - Preceded by ABC/XYZ Analysis
As a basis we have again our ABC/XYZ analysis. In our example for the month of February, it looks quite like that C* / Z* products make up the predominant share. In other words, products that in principle are not sold at all. In percentage terms, this therefore amounts to 42.3%. So if you look at the C/Z and C*/Z* products together, slow and yarn turners, that’s 2/3 of the total portfolio. This in turn means that I, as a retailer, need to take action. If you prefer to think in absolute values instead of relative ones, DataWow also shows you the value of the goods in another window. You also have a good number of so-called bread and butter items, i.e. those of which you should always have something in stock and which should therefore never run out, but with the slow to no-turn items there is also €66,000 of dead capital. This means a total of 1600 + 2,036 products.
Identify slow/no-moving items with DataWow to gain liquidity
If we now want to look at the area of “gaining liquidity”, i.e. exactly what we want, we can first subdivide into C/Z and C*Z* articles. You can see at a glance how many pieces of the respective product are still available and what the purchase and sales price is. For us, this means reducing margins and minimizing selling prices. This can be e.g. be made with a 10% discount.
Accordingly, you could set up a sales page in your store, where you place these slow and steady sellers and thus draw attention to the discount. The whole thing can then of course also be tracked with DataWow. If the items still do not get lost, go to 20% discount.
DataWow "Gain liquidity" - The detailed view
We can now take another look at the slow movers. In our example, we have an inventory quantity of 960 pieces of our product X, which in total represent an inventory value of 3360€. In the last 12 months, 652 of these were sold. A range of coverage of 17.7 months can then be calculated from the inventory quantity and the retirement quantity of the last 12 months.
That would then still be acceptable, but especially when individual products then stand out with 143 months and 506 months, you still have very many months of “fun” with it.
Questions about DataWow ? - Get in touch with eBakery or DataWow
Accordingly, after the analysis and the realization, there is a need for action for this retailer to bring these products to the man in order to gain liquidity, from which one can then purchase items again that run better and have a higher margin. If there are still questions or interest in the analytics tool for your online store, contact us or DataWow itself.
Haben Sie Fragen oder brauchen ein individuelles Angebot? Zögern Sie nicht, uns zu kontaktieren.
![ABC/XYZ analysis for online retailers with DataWow [Werbung]](https://ebakery.de/wp-content/uploads/bfi_thumb/abc-xyz-analyse-mit-datawow-pi9isldip49q222bwuaxjh7gj16nl7ba4dtji9ogzu-pilxjfp2s0gvfku7eqli3119mmwhnj2yn28pszhm2y.jpg)
![Ad-hoc analysis for online retailers with DataWow [Werbung]](https://ebakery.de/wp-content/uploads/bfi_thumb/ad-hoc-analyse-fuer-onlinehaendler-pi9isldip49q222bwuaxjh7gj16nl7ba4dtji9ogzu-pilxjgmwyui5r6su9904nisq80ruv86oz6w7a9g7wq.jpg)
