Collecting reliable data in 6 steps
Can you trust your data?
- Data Analytics
"There are three kinds of lies: lies, blatant lies, and statistics," said former UK Prime Minister Benjamin Disraeli. This also often applies to the use of data, because you cannot blindly trust data if you do not know the background.
Collecting reliable data
Every organisation collects data these days. Sometimes with a specific goal and measurable Key Performance Indicators, but also simply claiming "we might need this one day'. Online data is collected through the website using analytics tools and tag management solutions and through online marketing channels such as social media and email. But also offline data in, for example, a CRM or cash register system.
All these different data sources have their own structure, definitions, way of collecting and reporting data. They are often consulted by different employees (with different objectives) within your company. This quickly leads to different truths, because "we have to get rid of our suspicions and rely on objective data". The question is how objective it all still is when different people use data from different sources and interpret it all in their own way.
- One department focuses on Marketing Qualified Leads, the other department on Sales Qualified Leads. How many leads are there then?
- For Marketing, a mailing list registration is a conversion, while Sales only counts the actual sales as a conversion. How many conversions are now being reported internally?
- Customer Service has customer satisfaction as its objective, while Sales focuses on the number of new deals. Is the company successful if the number of customers increases, but customer satisfaction decreases?
- The data from team A's system differs from the data from team B's system, as both teams work with transactions.
Do you want to work data-driven with complete confidence in your data? Then you have to start with the basics. We will help you on your way with a step-by-step plan.
Step 1: Decide where you want to go and why
To be able to rely on your data, you must first ensure that your data is reliable. Sounds simple, but in daily practice this can be quite a challenge. You probably have your mission and vision on paper somewhere, but have they also been translated into your strategy? Do you have measurable objectives that have been translated into KPIs? And does your offer actually match the needs of your customer? So first map out the purpose for which you collect data.
Step 2: Determine your definitions
To properly interpret data, you need context and, above all, definitions. Because when you talk about the conversion rate on a website, what data do you use for that? Is it the number of transactions in one session that you consider to be conversions, or is it every session that had one or more transactions in it? And do you now divide the conversions by the number of sessions, or the number of unique visitors?
To ensure the consistency of your definitions, it is important that you determine and record them in a KPI framework. For each KPI, in addition to the definition, you also record information about the data source and data collection method.
Step 3: Structure your data collection
Your data is only reliable if you have carefully considered this:
- Your Key Performance Indicators (KPIs): which numbers are you going to aim for?
- The data quality: do you have a 'clean' database, for example?
- The data infrastructure: which data sources are there?
- The processes surrounding data: how is the data flow through the organisation?
- The tooling you use to collect data: do you have the right solutions?
- This way you ensure that you have everything you need to collect reliable data.
Step 4: Centralise your data
Using central dashboards that are accessible to the entire organisation, you create one truth. A sales employee will still look at the sales figures and a marketer at the ROI of the campaigns, but they suddenly also have insight into their influence on the total operating result.
Ensure that the dashboards are regularly updated and shared internally. In this way, everyone makes his or her decisions based on the latest and, above all, the same data.
Step 5: Leave data analysis to a specialist
Every employee within an organisation looks at the data with its own frame of reference. A sales employee is mainly interested in sales figures and the growth of the customer base, while someone from the customer service department focuses on customer satisfaction and the speed with which complaints are handled. Will your organisation still exist in five years' time if the number of customers increases, but customer satisfaction drops dramatically? Probably not.
A Data Analyst studies the data of your organisation as a whole. His goal is to provide insights and provide reliable advice based on data. A specialist in data analytics teaches your employees what to look for and how to interpret data. Data in itself is not useful at all. You must use data as input for stories and put the numbers in context.
Step 6: Enrich quantitative data with qualitative insights
If you have completed steps 1 to 4, you will have a wealth of information. However, data does not always tell you everything. Suppose data is telling a web analyst that the bounce rate is falling. Is that right, or wrong? Does it mean that visitors are quicker to search for the information they find, or are they confused and therefore drop out on your website? You cannot answer that question with data alone. Data is therefore often the starting point for more research. In the bounce rate example, you can, for example, view recordings of website visitors or retrieve information from feedback polls. Better yet: talk to your target group or analyse the actual behaviour through user tests, performed by a Customer Experience Specialist.
Are you already targeting reliable data?
If you want to be able to rely on your data, you need to know why you are collecting it, proceed in a structured way and analyse and interpret your data correctly. Do you want to know where your improvement potential lies in this area? Our data specialists are happy to provide you with insight into your position in our Building Blocks Model. Contact us.
This is an article by Rogier Kamer, Data Strategy & Data Academy Manager at Digital Power
Rogier is a pragmatic analyst to the bone. "Start with the basics and work from there to the future" is his credo. His heart lies in transferring knowledge and setting up processes to improve online understanding. In addition, he is always looking for opportunities to increase the value of online and to emphasise the importance of web analytics.
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