How do I measure success when using a Text Robot?

31. Dec 2020

At the latest when setting up a Text Robot, customers regularly ask themselves whether and in what way the success of using a Text Robot can be measured. There are at least four answers to this question:

1. Amount of text produced in relation to time

It is helpful to regularly generate a visualisation during the course of the project that shows how many copies have already been automatically generated by the Text Robot within a period of time. A so-called burn-down diagram, optionally also a burn-up diagram, is quick and easy for everyone to grasp. It shows in the x-axis the applied time and in the y-axis the number of automatically created copies.

To underline the success of speed in text production, this can be put in relation to the manual production of text. A copywriter manages between 5 and 10 product descriptions per day. With the help of such diagrams the extremely high scaling effect of a Text Robot becomes visible.

2. Money saved with automated text creation

Amortisation and scaling effect of a Text Robot

With the help of our amortisation and scaling calculator, after entering the optional figures, you can see how quickly the investment will pay for itself and what economies of scale will be achieved.

Usually a product description created by a text agency costs around € 0,13 per word or between € 7 and € 22 per product text. We have also met customers who pay € 25 ore even more per text. This is initially only the pure purchase cost of a text.

In the case of a full-cost accounting, there are a lot of additional items: time and effort for briefings, receipt of the copies, editing, coordination and approval loops as well as the setting of the text, for example, are very important as well. Including these surcharges, costs of € 17 to € 45 are easily incurred – depending on the characteristics, requirements and structure of the text.

It is advisable to convert the automatically generated amount of text into costs that have already been “saved”, thus making it clear how much money no longer needs to be transferred to text agencies or no longer accrues in your own company. In the event that this money has not yet been spent because no text has been written to date, these figures can be used to show the costs saved compared to manual text creation.

We have programmed an amortisation calculator which – fed with the key figures – calculates from which month on the investment into a Text Robot has fully paid off. Please get in touch with us for an individual calculation.

3. Development of the conversion rate

Measuring the success of using a Text Robot for automated content production

Text that optimises itself on the basis of reading behaviour and thus becomes increasingly attractive is no longer a science fiction scenario.

AX Sematics has developed a Javascript which you can integrate into your website in the form of a code snippet (special tracking code for Google Analytics). The analysis tool, which is currently still in the beta stage, is called “AXITE” and measures, for example, whether a website visitor has been shown the product text, how long it has been there and even which region the reader comes from.

Ultimately, all the data that can be collected with Google Analytics, is possible. Within a separate (and shared with the customer) Google Analytics account, evaluations can be generated that show a correlation between the displayed text and the target, such as the purchase (shopping basket). Text that optimises itself on the basis of reading behaviour and becomes more and more attractive with the help of the Text Robot is no longer a science fiction scenario.

Two text variants are generated by the Text Robot for each product and are displayed in an A/B test procedure. If, for example, text variant A prevails, you naturally want to produce more text based on the better variant.

The good news: In a further expansion stage, the knowledge gained from the A/B test is fed back to the Text Robot and new text variants are generated under the parameters recorded. This creates “a rule cycle” that automatically leads to better and better text over time. Self-optimising text – controlled by the reading behaviour of the users – will thus be created. This is an absolute novelty.

4. SEO and visibility in search engines

This part is very complex because both web visibility and search engine ranking depend on a myriad of factors and not even a good SEO consultant knows the algorithm used by Google down to the last detail.

Furthermore: popular measuring instruments like Sistrix (shows the visibility index in search engines) only draw on a small subset of keywords. The high variance of the automatically created text is not necessarily reflected here.

However, it can be expected that in cases where the previous product text was short or with a lot of “duplicate content” (marketing text of the manufacturers that are used as a product description), the visibility and ranking will benefit. It is advisable for an SEO specialist to accompany the changeover to automated text creation and – where necessary – to set the right course.

It is absolutely necessary to observe the following: Only a clean zero measurement and an analysis procedure documented before the project can lead to true future statements and comparative values. Changes during or after the project lead to misinterpretations and false comparative values.

Further contributions on the topic of Text Robots:

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