It is pretty much a given that great analytics can drive better decision making, inform product development and help identify where cost savings can be made. How to get there is the challenge!
There are two key types of data to consider:
Internal – management information generated by your ERP, CRM and other applications across the business
External – “big data” additional sources that augment internal data to provide true Business Intelligence
Sadly, many projects will falsely identify fixing all internal data as the first task and fail (think Einstein’s definition of insanity, doing the same thing and expecting a different result; you have definitely tried to fix your data before!). Here’s some thoughts on what you could do differently to create a data strategy for your business:
What do you need to know?
KPIs: What do you need to know to help drive your business?
Can you get that data?
Don’t groan, thinking about your Key Performance Indicators is a useful exercise! Let’s use lead generation as an example. With the race to drive web presence, is your metric how many people visit the website (quantity) or is it concerned with bounce rate (quality)? While this is simplistic, it’s a starting point!
Okay, so what if I am really interested in website visitors that came from a specific route (let’s say LinkedIn) and visited three pages before going on to buy more than £1,000 of goods - by measuring this, I can understand if my LinkedIn presence is working for me.
The data itself is provided by website analytic engines and your sales order processing application. So you have the data, brilliant! Now you need to unlock that data and present it in a way that is useful to you.
Having a data strategy helps you focus on what data is important. Now you start to understand what data needs to be reliable and stop stressing over the impossible task of keeping all of your data squeaky clean all of the time.
We made an assumption above, we assumed that we’re working with data we trust! Cleaning data is like clearing a loft, something you try not to think about and leave until forced by a big event (new system implementation or house move). Let’s extend the house move analogy and ask yourself this question: Last time you moved house, did you find an unopened box from when you moved in?
Part of your data strategy needs to include the housekeeping. If historic data is important then shape it up. If less so, then don’t sweat it. Either way, put energy into managing data going forward. Your existing applications should be helping with this. Minimise free text fields, use mandatory fields, workflows / approvals and make use of de-dupe tools that all help ensure your data smells sweet but only do it on the data you need to analyse (and where possible automate the capture), otherwise you will make your processes laborious and as water runs downhill, your users will look for ways to side-step.
Fast measuring and managing
Data analytics (and big data) are nothing new. Back in the 1950s businesses were using basic analytics, typically manually reviewed spreadsheets, to uncover insights and trends. It’s common sense that if you capture all the data that streams in your businesses, you can apply analytics and get significant value from it. So why isn’t it common practice?
The new benefits that big data analytics bring are speed, efficiency and accessibility. Whereas a few years ago a business would have spent considerable time / money to gather information, run analytics and identify those trends, today it is easier than ever to identify insights for immediate decisions.
The ability to work faster is what generates the ROI; there’s no return on data that tells you what you should have done, you want to know what to do next.
And whilst we’re on measuring, managing and ROI, think carefully about monitoring the cost. “Hot path” data is expensive to keep so consider what needs to be hot and what could be cold. Tools such as Microsoft Azure Data Services help you structure the storage and refresh options.
Don’t blame the tools!
Three years ago, a McKinsey Global Survey showed that business leaders expected their analytics activities to have a positive impact on company revenues, margins, and organisational efficiency. A revisit revealed mixed success: issues with strategy and tools were not the biggest culprits. As with any project, the key to success is in the tactics and people: leadership support, communication and right resource for the job.
The right resource is not always about the previous experience; the tools are easy enough to use with the right mindset and support to get to grips with them. You can (and dare I say, should!) “grow your own” resource and if you are engaging a specialist, remember the most successful projects are collaborations where the partner is developing your team (doing it “with you” not “to you”).
Once you have your home grown data champions, your team will refine the data strategy. This in turn will lead to further reduction in the manual tasks associated with data preparation and management, leaving more time to focus on higher value activities.
Identify a simple Proof of Concept
Dolphin versus whale, don’t eat the elephant etc. Before you try to do everything all at once, identify a manageable subset. Use the tools to spin it up, monitor cost and value and refine before scaling upwards and outwards.
Data storage is relatively cheap, working with it is more expensive; but with the volumes of data created it is a cost that can soon escalate. Managing the cost is where a good partner will add value. I do predict some “bill shock” stories for those who don’t provision their data storage effectively.
One of the really great things about how rapidly these solutions can be created is that you can run inexpensive proof of concepts to quickly check that your harnessed data is moving you in the right direction.
Discover how you can get started today by downloading our Predictive analytics facts sheet
The consumer culture has long outpaced the business world when it comes to evolving technology. We have hopped into an Uber, waited in line for the iPhoneX and indulged in affordable VR headsets as an easy wow-worthy Christmas present.
With the rise of Big Data, organisations are able to see how their customers really perceive them. B2C organisations are utilising Big Data when it comes to customer experience, however B2B organisations are still facing challenges when it comes to understanding the customer experience. Manufacturers can use the new approaches to data analytics which have been described by Dr Mohamed Zaki and Dr Benjamin Lucas from the Cambridge Service, to help them transform their customer experience and have a competitive advantage. Organisations have relied on Net Promoter Scores (NPS) and satisfaction surveys which are more traditional methods used to measure and quantify the loyalty of an organisation's customer relationships. These methods often fail to provide a true insight into the customer experience, meaning that organisations are lulled into a false sense of security, by which point is often too late to notice unsatisfied customers. Customer experience analytics allow managers to gain a much richer understanding of their customers and their interactions with the organisation throughout the whole process of the 'customer journey'. An article in Forbes states that "53% of companies are adopting Big Data Analytics, with telecom and financial services industries fuelling the fastest adoption". "The first change we had to make was just to make our data of higher quality. We have a lot of data, and sometimes we just weren't using that data and we weren't paying as much attention to its quality as we now need to" from Ash Gupta, Chief Risk Officer at American Express on big data and data analytics. 1. The jump to B2B A McKinsey report in 2016 highlighted the fact that B2B customer-experience index ratings are considerably lagging behind those of retail customers despite the B2B customer expectations increasing fast. The value of customers and customer loyalty within B2B organisations is paramount and so is increasingly important6 that customers do not become unimpressed with a lack of service innovation in the workplace. Within B2B purchasing, multiple stakeholders are usually involved in the decision making process and so organisations face a challenge satisfying their customers. In order to understand their customer’s experience, sophisticated analytical tools are required. 2. Big Data The value chain which has become increasingly digitalised allows organisations to have large amounts of data at their disposal Large amounts of data is useful, but only if it is analysed in an accurate and meaningful way. We need to know what questions to ask and how to ask them to make the most out of the data available Customer surveys tend to use scale questions which allows organisations to create metrics such as NPS, as well as text fields to allow respondents to input their own comments. Graph visualisations can be used to show the customer feedback, with individual responses coloured by the NPS categories. The metrics that are used based on customer surveys do not provide the organisation with an accurate image of the customer experience, whereas the individual comments that respondents make can be used to make detailed decisions. 3. Focus on customer experience Customer Experience allows for a much deeper understanding of customer decisions at each stage of the 'customer journey'. The cognitive, emotional, behavioural and social dimensions of customer behaviour are utilised and incorporated into a framework for the customer data which allow a more functional measurement of how well a service or product is performing. When these dimensions have been defined, they are incorporated into the data set and machine learning will allow the scoring of customers on only aspects that matter to them, therefore are more tailored to the individual customer. This data allows organisations to pinpoint any critical data that highlights any underlying issues along the 'customer journey' and provide insight as to how and where an organisations needs to make changes in order to improve their customer satisfaction and experience. Greater connectivity comes greater expectations It is becoming increasingly important that manufacturers learn how to understand, manager and measure customer experience. These new analytical approaches give manufacturers the opportunity to lock in their loyal customers with richer, more accessible, more bespoke and more responsive services. The challenge comes from not creating and providing a service that your customers require. See below for additional posts relating to manufacturers and how they can gain a competitive advantage: