How Big Data Analytics Can Help Your Business Grow
Increasingly, we see the value of big data analytics and its importance in contributing to business success and growth. This guide by our analytics experts will discuss the capabilities of big data analytics and the value of a big data strategy in optimising a business for growth.
What is big data analytics?
Big data on its own is essentially nothing more than massive amounts of disparate data sets, which are simply too large and that are evolving and changing too quickly to be easily analysed and put to use with traditional tools. Big data has developed from the early days where we considered millions of transactions and line item data of a large retailer to be big, to an exponential increase in data volumes as a result of the rapid digitisation of commerce (online shopping) and the explosion of social media as the new main channel of information consumption. Digital commerce and social media are creating more data a day than a large retailer creates in a year.
Big data analytics is the art and the science of harnessing this new commercial asset and mining it for valuable nuggets of information that a business can use to support their ambitions.
Why is big data analytics important?
Big data analytics is important because the benefits that a business can make through the smart application of its big data can be wide reaching in terms of generating incremental growth and enabling massive operational efficiencies that drive up profitability.
Central to this is the power of big data to help businesses better understand their customers. The better they know what their customers want, how and when they want to buy and do this through an experience the customer loves, the more their customers will shop with them over and over again. The more they shop with them the bigger advocates they become for the brand and the more they share this with friends and family who then start to shop with them.
Big data enables a business to put their customers at the earth of what they do and reap the rewards.
Benefits of big data analytics
Our big data strategy consulting team are continuously working with businesses to use big data analytics to help them understand their customers better and use this information to direct and inform their strategic objectives. With these clear objectives, our big data consulting team can then get to work helping identify how best the business can use their big data to support these objectives. We find there are typically five areas where the benefits of big data can be found most often:
1. Identifying opportunities and growth
Big data allows you to understand patterns in purchase behaviours and product choices, which can be used for plotting where customers have holes in their baskets i.e. products they might buy if available, or identifying ways to get them to come back more often or buy slightly more. Commercial teams can use these insights to supercharge their ranging and promotional strategies. Likewise, changes in purchase patterns can be early signals of customers switching to competitor brands and the CRM team can swing into action with remedial actions to retain customers.
2. Product design and innovation
Behavioural data is generated every time a customer makes a purchase, clicks on a web page, etc. and together these data footprints can be used to generate patterns of behaviour. Using additional data sources, such as product metadata, data scientists can start to build analytical models that help identify and classify the needs and motivations behind purchases.
A really simple example of this might be that a customer that only ever buys ready meals may be classified as someone who is time-poor and not interested in cooking. These insights can be powerful ingredients to the product design and development process keeping your products fresh and meeting the latest needs of your customers.
3. Customer experience
Customer data, be it the route they have taken through a website before they drop-off or make a purchase, or their social media posts, offer powerful insights into what customers enjoy about a brand and what is not working. With the right big data analytics tools in place these alerts or triggers about the experience can be notified to the business in near real time, enabling them to quickly rectify the situation and continually improve the experience and brand reputation.
4. Operational efficiencies
For many businesses other than their advertising the next two big cost items are staff and physical stores or branches. Optimising staff scheduling and opening times offers businesses the opportunity to dramatically increase their operating margin and reduce wasted resources.
This optimization takes two forms. Firstly, making sure stores are open and adequately staffed to meet customer peaks and troughs in demand and secondly, to ensure the right skills and channel mix are targeted to the relevant different customer groups to optimise sales conversions.
5. Enhanced risk management
Big data by its nature is perfect for spotting anomalies in transactions or events that look different to the norm. Finding and investigating these discrepancies in activities is an extremely effective way to spot and prevent fraud. Similarly the large volumes of historic data make it very good for looking at historical patterns such as sales and how previous conditions (such as weather) impact these. This capability is what underpins forecasting models that businesses can use to better plan their activities and reduce the risk, for example, of producing too much or too little product for sale.
Examples of big data analytics
Big data analytics is used everywhere these days. Below is a sample of some of the ways our clients are using big data:
Global Distribution Services (GDS) – Travel
This client processes billions of travel bookings (air tickets, hotels, car hire) for thousands of travel agencies across the globe. This creates a huge amount of data about when and where people are travelling around the world. Taking this data we have helped them create a smart insights solution for their travel agency clients to find out more about travel trends of their customers and their competitors' customers. This helps them to figure out the best products and services to market and sell to their customers. This helps the travel agents to sell more products, which they do through the GDS platform.
Payments Network – Finance
Our client in the Finance sector processes credit and debit card transactions, on behalf of millions of consumers, for merchants across the world, creating a huge data store on the individual shopping patterns and brand preferences for each of these consumers (anonymised of course). This data is provided back to these merchants in the form of value-add insights, which tell them more about their customers and help them deliver more relevant products and experiences to their customer base. This encourages more electronic transactions with the payment networks merchant base driving up overall transaction volumes and therefore revenue.
B2B Credit Products - Finance
This B2B Finance provider targets small to very large businesses that need to purchase vehicles and fuel as part of their operations. It provides card products that can be used to buy fuel at preferential rates in any forecourt across Europe. The business model for the product relies on the volume of fuel being purchased through the cards and the credit charges resulting from these transactions.
It is a highly competitive market that results in regular switching between brands based on price offers. Using big data analytics every individual transaction is tracked and each individual client's purchase patterns are monitored to spot potential signs of attrition. As soon behaviour changes are spotted the client teams can swing into action to work out how to keep the customer.
Sales Operations – Retail
This large home furnishings retailer relies heavily on the effectiveness of its sales teams to help customers through the product selection and sales process. Each sales person is given specific sales targets with the amount of products and add-ons they need to sell. Using big data we have a near real time solution that tells each sales person how they are doing and what they need to do to hit their target – a bit like a Fitbit for sales people. This not only makes sure each sales person knows what is required to get their bonus (and knows it in time to do something about it), it also provides additional team engagement and motivation through the gamification of the target setting.
How do businesses use big data analytics?
Using and getting value from data is notoriously challenging for all kinds of reasons, from managing the sheer volume and variety of data, to creating the right environment and buy-in from the business to adapt and make change.
One way to overcome these challenges is to take an agile approach to introducing and embedding the data strategy and big data operations into 'business as usual'. This approach takes traditional consulting practices and makes them more relevant for the world of big data analytics.
In essence it splits the strategy implementation into three distinct parts that can run sufficiently independently, yet work together and build off the progress and learnings made as capability and experience builds within the business.
A short summary of this approach is as follows:
Implement quick data wins (pilots) to secure immediate returns and learnings:
These can come from all areas of the business and typically focus on areas that can be quick to change and realise value. E.g. optimising promotions, reduced marketing costs and productivity improvements.
Quick-wins start the process of buy-in and belief/trust in data across the team. As such these need to be fast, low risk yet high impact wherever possible.
Importantly they also start to illustrate to the business the practical requirements for rolling out the capability across the wider business.
Design the go-to operating model for data analytics:
This begins soon after or in parallel to the ‘quick wins’ work.
This starts with developing the portfolio of use cases and vision for data, which provides the direction for the design of the organisational capability to deliver.
Design of the operating model not only encompasses the people and tools required, but also the wider picture of data governance processes and systems architecture and infrastructure decisions.
Plan and implement the organisational change required for adoption:
With learnings in place and a clearer view on the optimal operating model for the business, the business can then start to work on wider adoption and change.
This part of the approach considers how the business goes about preparing and planning for the business to adopt and adapt to a new set of processes, methods and actions.
Arguably, this is where the rubber really hits the road as it deals with driving out the wider use and adoption of data driven thinking and decisions across the business.
Big data analytics best practices
Best practice in Big Data Analytics is a huge topic that can go extremely broad and deep across the multitude of functions and activities with which it intersects.
During our big data strategy consulting engagements we are often asked what are the top things we have learned that are most likely to lead to successful big data outcomes. We have six things we think consistently matter:
Don’t let the job of handling huge data volumes and variety cloud the issue – get started by identifying the most relevant data for the job in hand to unlock complexity.
Never underestimate the destructive power of confusion through conflicting data insights. Decide on the top 10 or so key metrics that matter and deliver a single version of the truth across all information points for these. This will help everyone stay on point and facing the same way.
Do it nicely, but break through the data silos in the business early and don’t spend too much time tip-toeing around territorial behaviours. Releasing the data from these silos to where it really matters not only enables the business to take full advantage of the power of the big data, but it also fuels cross-team collaboration where it is needed most.
Focus on the people side of the data strategy or data programme as much as the data itself. Quickly establishing the appropriate collaborative environment and governance that cuts through barriers will help to enable fast and simple deployment timeframes, which go a long way to making sure data projects don’t drag on too long leading to impatience and disappointment from the stakeholders.
Start with low risk, fit for purpose tools (we recommend Opensource) so teams can focus on getting on with the job in hand of creating value from data. Data strategies often fall over because of poor decisions taken early on in selecting (and becoming locked in to licence agreements) expensive tools that do not meet all the needs of the business. Opensource options are extremely well supported with amazing online communities meaning less training and more time and budget to put your data to work.
Point 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. (Read up on the concept of Marginal Gains as these are hugely relevant to achieving success in big data operations).
Big data analytics: future predictions
Big data is only going to get bigger and bigger as more and more businesses migrate to the cloud, and internet usage continues to explode with billions of internet connected devices throughout our homes and working lives.
This growth in data volumes is going to fuel the need for more and more machine learning. More data will mean more applications for its use. The sheer volume already makes many human-reliant analytics models impractical and machine learning will become a critical component of big data automation.
This is going to continue the growth in demand for Data Scientists and data experts. Businesses will need to build up their own in house expertise, but also recognise that they are likely to build teams of generalists that can manage the ever increasing data operations that will be central to their business. Demand for data specialists who can support business from the outside by building new solutions using increasingly specialised skills will be outstripped by demand.
Governance, security, privacy, and fraud are going to become massively more important driven by the growth in internet use and commerce. Data Scientists will need to become ever more sophisticated to tackle the growing issues of cybercrime and 'fake news'.
For more insight on the application of big data analytics you may wish to read our recent article on How to Utilise Data Analytics Effectively. To discuss big data consulting contact our team of experts or find out more about our services online.