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Do you use your data?

15th June 2021

Author Alex Waleczek

The benefits of ‘analytics’ over ‘reporting’.

Virtually every system we use today generates data, some more obviously (like entering your personal details when creating an account somewhere), some less obviously (like the website that tracks which pages you opened and how long you looked at certain products). 

We work with this kind of data every day. We use CRMs to manage our customers and opportunities, ERPs to manage production and distribution, and a billing system to keep track of the financials. In many cases, we think of data as data points. When opening an opportunity in the CRM, we may see the type, age and value of the opportunity. In the ERP we may be able to see stock levels for a certain product. 

But once the opportunity is closed or the stock is gone, we don’t really care about it any longer and move on to the next item instead. We may go back to it to look something up or want to make sure that the closed sale turns up in our monthly review but once it’s out of sight, it’s often out of mind. 

The big deal about data (i.e. the collection of individual data points) and the reason everybody wants a lot of it, is that it often stays valid and relevant for much longer than each individual record. Data can be analysed and mined, and it can teach lessons and inform future decisions; but only if you make use of it. 


Do you truly use your data? 

What is happening with all the data in your CRM? It is probably used to figure out if targets are reached, to determine company performance, and whether the opportunity pipeline looks good. All these activities are reactionary. We look at the data that is in the system and determine if these are good or bad numbers. Then we go off and try to create better numbers for the next period. This is a typical reporting use case (opposed to analytics). Reporting is necessary and useful but doesn’t utilise the data you have to its fullest extent. Analytics can help the business to make informed decisions and evaluate the performance of certain approaches and processes across functions.  

A single salesperson may have an understanding of what works well when selling their products to their clients in their region. They may even talk to other salespeople in the business, but what if there was a process that could collate all this knowledge and makes it instantly available to every salesperson and even other teams at the click of a button? 

A single salesperson might also have a feeling that something in the market is changing but may struggle to elaborate on this feeling or even be dismissive of it since they can’t prove anything. Imagine if there was a process that could reliably identify these changes and alert you of them… 


Analytics to the rescue 

These analytics functions look at the data ‘as a whole’, try to identify trends and whether it’s possible to learn from these trends for future questions. The big benefit of analytics is that it can ingest large amounts of data and test a lot of hypotheses in a very short time. Your business probably knows about its seasonality in sales, probably for individual product categories. But are you aware of differences in seasonality for different parts of the country? Can you identify different sales patterns in two stores at opposite sides of the same city? Or know at which hour of the day people are most likely to buy a certain product? Analytics provides the ability to go to the lowest level of detail to find relevant information and apply it to your business processes to help you optimise them.  

You have all the data to do exactly this already. If you use Salesforce or Tableau, you even have a platform that allows you to do all of that! 

Predictive scoring 

One of the easiest (and at the same time hugely powerful) use cases is predictive analytics. With Einstein Discovery and Tableau’s “Business Science” approach, getting predictions is done within a few hours. You can judge the likelihood of a sale to close or a customer to churn without a highly qualified data scientist involved. Once you have – for example – a churn score created, Einstein Discovery will tell you the action with the most significant impact on the likelihood of a customer to churn. It might be as simple as giving them a quick call to check if everything is alright and churn may be avoided. 

There is incredible potential with a feature like this to increase customer satisfaction and company performance. 

Scenario planning 

Another use case that traditional reporting can only address with a lot of customisation in an Excel worksheet is scenario planning tasks. What if your margin was slightly higher? How many more customers do you need to acquire to reach your target revenue? These questions usually require complex queries and the ability to quickly adjust assumptions to see the impact they would have. Tableau provides the ability to connect to your various systems to combine the data, to dynamically adjust parameters and to calculate the outcome on the fly. All while automatically using the latest data and giving you the ability to store scenarios to refer to them at a later point in time. 

Anomaly detection 

When people in an organisation realise that something went sideways, it’s usually too late and the only thing that can be done is damage control. If you have a system, however, that continuously observes your sales figures or the usage statistics of your service, it can very reliably detect a diversion from the normal state and alert the appropriate people. They can then monitor the situation and prepare to act, giving you an advantage over your competitors.

Business automation 

These examples assume that there is a person that interprets the output of a model and acts upon it accordingly. Taking it one step further, though, these things can be automated. If a customer is likely to churn because you haven’t been in touch with them, the system can send them a customised message and ask for feedback. If applying a discount on a Wednesday evening is predicted to boost sales, the system should trigger targeted ads at that time. The amount of micro-adjustment that can be made based on any of the parameters is virtually limitless compared to the number of adjustments a single person or even a team could make on a regular basis.  


Just the beginning… 

Salesforce’s Customer 360 vision often focuses on single data points for specific interactions. With analytics, you utilise all the data you have across systems and functions in order to provide value to your customer, to keep your organisation at the top of their minds and to increase the performance of your organisation. It does not replace analysts or data scientists, but it takes the grunt work away from them so that they can focus on more valuable use cases. Having a skilled person just do what a system tells them and do these tasks repetitively is a waste of their time and skill and, in the worst case, your organisation will lose them. 

Analytics platforms can help with these problems; their ease of use allows you to test assumptions rapidly and very quickly prove the value they provide.  


If you would like to discuss your options or would like to understand how these features could be applied to your business, please get in touch at


Alex Waleczek is Davanti’s Practice Lead – Analytics. 

In his role he is responsible for all things data. Having worked as a consultant with Tableau in the analytics space in New Zealand for the last six years, he is very familiar with the problems companies face when trying to make sense of their data and how to solve them to bring our clients closer to their clients. When he is not working on Tableau for Davanti, he is blogging privately about all things data and hiking New Zealand. You can get in touch with him via our contact form, LinkedIn or Twitter.

Alex Waleczek