How To Utilise Data Analytics Effectively

Data analysis is a broad term used to describe the process of exploring, manipulating and enriching data sets to derive information and insights.

 

This information might be simple descriptive information such as telling you what your sales were last week for a particular product or a more insightful set of behaviours that might lead to a hypothesis that explains a customers particular need and preferences.

What is meant by data analysis?

Data analysis has been around for years and regularly gets referred to by many different terms that are popular for a period of time.  In reality, it is just the same as the maths and statistics we learned at school.  Historically the really clever analytics would be taught at a higher level and was out of the reach of many of us.

 

Today however, big data analytics can be done by almost anybody using familiar tools such as excel, through to more complex coding languages such as Python.  Many of the very complex machine learning techniques still require a strong technical background in analytics but many of these can now also be delivered through user friendly analytics applications such as Tableau or tools kits from providers such as AWS.

 

Data analysis is used in a very broad range of business applications to research their markets, understand customers and inform their decision making. The applications are endless whatever your field.  In marketing and customer experience for example, data analysis is now core to developing customer insights, targeting media buying and marketing messages through to developing new products and evaluating campaign performance.

 

The data used for analysis can be any data and is often historical basket, transaction and web behavioural data collected each time customers shops.  Businesses can also call on lots of third party data that they can append to their own data. This is usually modelled data and is often used to build a deeper understanding of customers and markets.

How is data analytics used?

In our experience of data analytics consulting there are a multitude of uses for analytics. In fact we would be hard pressed to find any part of a business that would not find some benefit from data analytics.  But broadly speaking there we can summarise these into four big areas that we come across time and time again in our analytics consulting work.

Understanding customer needs and behaviours

Almost without fail this is the starting point of most data analytics journey.  Data provides unparalleled opportunities to understand so much more about your customers. 

 

You can identify when and where they shop with you and when they do, what they buy.  Delving deeper you can understand the attributes of the channel they use and the products they buy to give you deeper insights into what their needs are. 

 

You can move away from treating every customer the same and pitching your products and services in a highly tailored fashion to supercharge your sales. 

 

The better you understand your customers the more relevant you can be to them and they will shop more with you and hang around for longer.

Making better decisions, faster

Driving media spend and marketing effectiveness

For most consumer brands at least, media spend and the marketing budget are big ticket items on the company P&L.  Alongside your improved customer understanding, which will help you work out which customers are the most valuable and where to find them, you can use data to optimise media and marketing spend. 

 

This insight can lead to reduced wasted spend on media and marketing channels that don’t deliver profitable customers and more targeted spend on effective local channels that cost substantially less than broad brush national media. 

 

For one of our analytics consulting clients this meant a total reduction in marketing budget of 20% with increased effectiveness. By creating a clear view on their catchment areas (which stores serve which customers) and by analysing spend behaviours and how they correlated with local demographics, they were able to pinpoint which of these areas had greater potential, or headroom for more spend. 

 

By overlaying their above the line media plan, which was predominantly TV they could identify which TV regions they could safely reduce spend in without adversely impacting footfall.

In today's competitive world, consumer brands and B2B brands alike need to be able to react quickly to changes in the market – especially with threats from new market digital only entrants who come in and undercut. 

 

Data can make this happen with continuous monitoring of customer behaviours to spot switching and being able to quickly react with competitive offers straight to the customers that matter without diluting your margins.

 

In sectors such as retail this is hugely important to the commercial teams that need to monitor sales promotions closely and adapt fast.

Making operational efficiencies

With great clarity on your customer behaviour you can use this to seek out cost savings and efficiency improvements in your business. 

 

This can take many forms but regular areas we work on with our analytics consulting clients include optimising store location, driving efficiencies in workforce scheduling across the stores and call centres and optimising the digital channel experience to drive greater online conversions.

What is meant by data analysis?

Effective use of data analytics has become a core business competence for almost any business.  Thanks to the advances in data storage and processing the volume and importantly the quality of data an organisation has access to has increased to the point where the time available to be spent on developing insights and solutions from data has overtaken the earlier challenge of simply gathering information.  

 

In effect it has moved into the realms of business as usual and this means the functional and technical challenges of the past 10 years are largely solved.  Now the pressure is on for businesses to really show their mettle in their use of data analytics to deliver business benefit.  This is a challenge for all leadership and not just the IT department.

 

There are a number of key areas now where leadership should focus to ensure their business is making the most of the opportunities from data:

  • Leading from the top – ensuring the C-suite are able to effectively embrace data as a core capability of their role.  This requires the recognition that training and coaching is required at the C suite level and beyond.

  • Driving awareness throughout the business about the power of data, the art of the possible as well as the dangers.  Everyone from the shop floor to the warehouse needs to understand how and why data can help them to do their role more effectively.

  • Democratising and sharing of information and insight to breakdown silos and empower individuals to take informed decisions in the interests of their customers.

  • Creating a culture of innovation and that failure is and acceptable form of progress so long as change happens through a measured process of test and learn.

  • Focusing on the agile, quick wins where successes can happen at pace and momentum for change is maintained, minimising cynicism and wear out.

  • Recognise that actionable analytics drive change in process so this needs supporting through effective change management as much as great data science.

  • At the same time teams need to be empowered and safeguarded with the proper training in data governance and safe use so they all take on the responsibility of respecting their customers' privacy and rights.

How data is stored and managed in organisations 

Data storage and management is a critical part of big data analytics and getting this right for your organisation can make or break your success with data.

 

Some organisations are still using traditional in house/on premises data solutions but it is becoming increasingly accepted that using cloud based solutions such as AWS, Microsoft Azure or Snowflake offer many great benefits in their ease of use and ability to scale.

 

Whichever model is used, the key to effective data management is setting out the right data management and governance policies and processes in the data strategy.  This needs to deliver on solid data security and governance to protect what is an increasingly valuable and sensitive asset. 

 

However, as much as providing the controls, effective management and storage of data needs to foster an environment of enquiry, discovery and collaboration across teams.  This means making the data accessible, available and usable by teams so they can innovate and trial new data applications.

What are the types of data analysis?

Data Analytics is typically described using four main categories:

Descriptive analytics is the kind that you would usually see in reports. It consists of defined metrics that the business understands well and describes what has happened in the past.  For example, sales have declined year on year.

Predictive analytics look to the past to help predict on a probabilistic basis what is likely to happen.  This relies heavily on machine learning techniques and has enormous potential for business to act as an early warning system.  This kind of analytics is where true payback begins to happen. For example, we developed an attrition prediction model for an analytics consulting client of ours that can predict a customers likelihood to leave up to six months away.  This gives them an enormous runway to address problems and work to keep the client happy and stay with them.

Diagnostic analytics is the natural next step on from descriptive analytics and looks to understand why something has happened and identify the drivers. This relies on standard techniques such as principle component analysis, regression and correlation.  For example, it will look to understand what has driven a decline in sales such as reduced customer numbers or switching to lower priced products.

Prescriptive analytics look to provide a recommendation or answer to a situation. The best known examples of these are product recommendations based on a customer's purchase habits. These solutions are where techniques such as neural networks and simulation modelling come into play.

If you would like to discover more about how data analytics can help your business, contact our team of experts or learn more from their published analytics insights and business guides, including our detailed guide on Data Science vs Data Analytics.