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Challenges of Getting Value from Data

Businesses often struggle with getting value from their data.  It’s a well-accepted and proven idea that using data analytics to drive decision making and power your business through automation and self serve through digital channels is a natural requirement of any decent sized business today.

It has been well researched that effective use of data can double your chances of being in the top quartile of financial performance in your category, or being up to 5% more productive and more profitable than your competition.
 

That aside, since the onset of the global pandemic and ensuing seismic shift in both consumer and working behaviours, if businesses are not digitally enabled they are effectively cutting themselves out of the majority of your addressable market. If a business is digitally enabled and not using their data effectively, either they have something no one else can offer or they are throwing good money down the drain.

The key challenges businesses face when getting value from data can be summarised into the below five categories: 

 

  • Data Paralysis – This occurs when businesses have collated so much data they don’t know what to do, or where to start.

  • Difficulty differentiating data - With so much information being created from data,  it can become hard for businesses  to collectively see what is happening and decide what aspects of the business to focus on.

  • Data Silo Stalemate – This occurs when traditional organisational structures have created silos of information ownership that are challenging to break down.

  • Silver Bullet Idolatry - In this situation, building on the hype of the “Big Data Cheerleaders”, business executives set out to change the world with substantial data projects rather than seek growth through continuous improvement across every facet of the business.

  • Impatience - This can lead to challenges, as short term thinking for quick wins can detract from the truth that continuous data-driven improvement builds dramatically enhanced performance over time.

 

A data strategy should look to navigate a business through all of these challenges, and in particular it plays a huge role in bringing the business together with a common understanding and mission for data including:

 

  • Providing insight into the common goal and purpose of data in the success of your business.

  • Showing businesses the logical and most effective order in which to build capability, allowing for scalable and repeatable results. 

  • Bringing the data to life through a common language that is curated and shared across departments so everyone knows what they are talking about.

  • Driving awareness and respect of the intrinsic value of data to the business, likewise the value it has to your customers right to privacy and personal security.

Above all, a data strategy needs to set the tone for how the business sets out and develops its capability to find solutions to the challenges by:

 

  1. Identifying the most relevant data for the job in hand to unlock complexity.

  2. Delivering a single version of the truth across all information points.

  3. Releasing the data from silos to where it really matters and can make a difference thereby enabling effective decision making at the point of action.

  4. Establishing the appropriate environment and governance that cuts through barriers to enable fast and simple deployment timeframes eradicate impatience and disappointment.

  5. Enabling the teams with fit for purpose tools so they can focus on what’s needed for the job.  This would mean less training and more time and budget to put your data to work.

  6. Direct the business to where the quick achievable wins lie as incremental gains are more readily achievable, maintain momentum and add up to greater results over time.

How big data analytics helps businesses increase their revenue

The main revenue drivers from using big data come from the commercial side of the business.  The key ways this is achieved in consumer facing businesses typically falls into the following categories:

 

  • Making sure the right product mix is on sale that both attracts the customer through the door and to encourage additional purchases whilst there.  This requires understanding the core needs of the customer base and how they intend to use products, making sure then the product mix fulfils this need.  For example, in a DIY setting you want to fully understand the type of projects your customers are undertaking and making sure you are providing everything they need to complete them.

  • Ensuring the prices of the products on offer are competitive and within the budget of the customer base.  This needs an understanding of the price sensitivity of customers and making sure that a business is offering the right product from those on a budget - to those looking for a more premium product or experience. 

  • Making sure promotions are not just encouraging customers to stock up on what they would normally buy because it is cheaper than usual, but also getting them to trade up to a higher margin product or encouraging them to buy from a new category.

  • Understanding what keeps customers happy and returning is another key revenue driver.  The cost of acquiring new customers is usually high, so staying on top of how to best keep them coming back time and again is critical.

What do we use business data for?

Aside from these commercial aspects or use cases for data, businesses are now using data across every part imaginable from Sales Operations, Marketing, and Customer Service through to Manufacturing, Logistics and Supply Chain and Finance and HR.

 

The overarching reason they use data is to make better decisions and these decisions appear in every walk of business life.  Data collects together everything that has happened with a customer, a product or store, and stores this information in a way that it can be aggregated together across many dimensions and analysed to spot behaviours and trends.  

Business data

Data allows a business to formulate a fact based view of what is going on, rather than relying on individual experience or gut-feel.  With this in mind it can then use that same data to identify what the drivers of performance or  behaviours are.  

 

For example in the simplest form, a business owner may ask “are my sales declining because my customers are buying less or I have less customers?”  Understanding the drivers behind performance provides the crucial direction a business needs to take when figuring out how to grow sales. If it is because their customers are buying less or they just have less customers could mean two very different types of problems to solve.  It might mean that their existing product range is now no longer meeting their customer needs and they are going elsewhere or it might mean your marketing is no longer working and you are just attracting less people to your brand. Both scenarios require further investigation and analysis - but without the data to guide identifying root cause and pinpointing where to begin resolving the problem performance can be next to impossible.

Examples of big data analytics

These types of big data analytics can be used throughout a business and below we have listed a few examples of how are customers are using it.

  • An international travel platform integrates all the data from its many thousands of travel agencies to provide market insights and competitive benchmarks that allow its clients to identify new opportunities for growth.

  • A supermarket brand consolidates all the shopping trips of its customers and uses these to understand how its customers are shopping each store and make design improvements based on this intelligence.

  • A home goods retailer combines its own customer transactions and media spend data with market data from its financial services provider to spot where it can target local markets effectively without the high cost of national media channels such as TV.

  • A DIY retailer use customer shopping behaviours and product choices to bring the customer and fact based evidence to the strategy table in defining what products and promotions it runs during peak seasonal periods.

  • A travel agency brand uses customer behaviour and propensity modelling to optimise how it sells holiday add-ons to customers once they have made their initial booking.

Most valuable types of data

The most important data is your transaction data and their purchase behaviour data (how they have purchased from you through the web). Many businesses like to believe that collecting customer data is the most important and most valuable kind of data. In some ways this is true, as it obviously allows you (permissions withstanding) to connect with customers and continue to sell to them.  However, as consumers we all know that being bombarded by emails and messaging from brands can be extremely intrusive and irritating and can quickly turn us off a brand.  

 

Whilst having contact permissions with customers is an obvious way to market to them it is more important that business focus on letting the brand and customer experience speak for themselves such that they are enough to keep customers coming back without resorting to the old email campaigns.

How Beyond Analysis use your data

We focus on anonymised data such as transactions and web behaviour.  Using this data can tell us exactly how customers are behaving and interacting with a business and what is ultimately driving a sale and a repeat visit.  This is all about how we can improve and optimise the experience so the customer becomes an advocate and returns on their own accord.  This is in our view the holy grail for data.

 

If you are interested in exploring the challenges of getting value from data in depth please contact our expert team or read more of their insight articles online.  You may also be interested in our recent analytics applications articles including How to Improve Customer Experience with Predictive Analytics.