Data intelligence vs Business intelligence
There is always a new buzzword getting thrown around in the world of sales; trending terminologies that can be difficult to navigate and understand. The latest being ‘data intelligence’, and whether or not this is different to business intelligence.
Data intelligence definition:
“The analysis of various forms of data in such a way that it can be used by companies to expand their services or investments. Data intelligence can also refer to companies’ use of internal data to analyse their own operations or workforce to make better decisions in the future.”
Business intelligence definition:
“The strategies and technologies used by enterprises for the data analysis of business information. Business Intelligence technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies include reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics.
BI technologies can handle large amounts of structured and sometimes unstructured data to help identify, develop and otherwise create new strategic business opportunities. They aim to allow for the easy interpretation of these big data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability.”
Does it really matter?
You might look at these definitions and think so what? – its all data, and it’s all about what organisations do with that data. My response would be, “I agree”! After all, data is really only valuable if it helps sales teams, and the overall business, make better decisions.
Your organisation might be sitting on the world’s largest data pile, but it’s useless unless you have the means to translate it into insights that drive your sales and business strategies.
What really matters is understanding that, regardless of the buzz, being competitive in 2018 means transforming data into insight that add real value to the sales person (and the ultimately, the customer).
Not just gathering data, or data mining, but using emerging capabilities in data analytics, data modelling, machine based learning (MBL) and natural language processing to interpret data, garner value from it, and develop smart sales strategies.
Turning data into insight
As more and more information, opinion and hard-data has been generated and held in a vast myriad of places – Facebook, Twitter, LinkedIn, blogs, news-feeds and web-sites – it’s possible to find out just about anything about anyone. A veritable goldmine for sales professionals looking to meaningfully engage with prospects.
The problem most organisations have is extracting the important chunks of information from all the ‘noise’, to deliver the level of customer insight that sales teams need to win new business, grow their existing customer base, and differentiate by experience.
Despite the term being coined over 20 years ago by the ‘Father of Customer Experience’ Lou Carbone, ‘customer experience’ is still perhaps the most exciting opportunity for B2B sales today.
According to a 2017 Walker Study, customer experience (CX) will overtake both price and product as the key brand differentiator by 2020. This sentiment is reinforced by the likes of Deloitte and Aberdeen, who have both produced statistics on the value of customer experience and demonstrated that it is both a key differentiator and retention strategy.
Insight is the lifeblood that drives customer experience and sales success today, and those investing in and adopting strong insight-driven approaches are likely to steal a march on their peers over the next 3-5 years. This is something we are certainly seeing within our own customer base here at Artesian Solutions:
• Metrobank – As the UK’s fastest-growing high street bank, they have grown to over 900,000 customers in just five years by ‘creating fans not customers’. Their insight-driven CX approach underpins every aspect of their business. They consistently uncover commercially valuable insights which deliver great customer experience opportunities. This helps them grow their lending and deposit books, win new business and reduce customer churn.
• Qlik and NetApp – These technology businesses remain relevant in perhaps the most competitive and fast-moving market of all by focusing on consistently adding value. Both companies constantly monitor markets, prospects, customers and competitors to uncover insights that help to inform messaging, products and, ultimately, improve the customer experience.
• JLT – This insurance giant is using intelligence technology to offer new experiences that will change how we look at insurance. In the past, interactions with insurance companies only happened when we had a problem or at annual renewal – neither one being the basis for a positive experience. But by leveraging a deep understanding of their customers, JLT can uncover opportunities, proactively respond to risks, and build more personal relationships throughout the lifecycle of a policy. They are enabling their people to operate more efficiently, and be first to address emerging insurance needs.
The common thread amongst each of these organisations is that insight underpins every aspect of their sales strategy.
They have taken up the data intelligence/business intelligence challenge and are using advances in software development to analyse, filter and present the insights of greatest value to sellers from the almost unfathomable amounts of data created every day.
Then most importantly, they act on it….
Turning insight into action
Data intelligence/business intelligence software vendors have certainly made big gains when it comes to generating insight. But how do you put that data and insight to good use?
This is perhaps the area where much investment is now happening, with vendors looking at how data, and the insight generated from it, can disrupt how sellers actually sell.
Emerging capabilities in data analytics, data modelling, machine based learning and natural language processing are no longer just filtering data into insight, but interpreting data and directing action.
Let’s look at a few examples:
For several years now, savvy sales teams have been using prospecting tools and lead-scoring software to help them find and triage leads more successfully. The most sophisticated have been combining this data with information from the company’s own sales experiences.
The problem is much of this is static data, offering little insight into what is actually going on with a customer in real time.
Artificial Intelligence (AI) models seamlessly augment static, cold data with real-time insights and real-world events, ensuring not only is firmographic data bang up to date, but also helping to uncover previously missed opportunities and potential new revenue streams. By using AI to gain an understanding of experiential factors from the news, views and opinions the company has been generating, a seller can achieve a valuable 360-degree view of the customer ecosystem.
The result is a much more targeted and accurate set of measures and triggers that can be used to perfect lead generation and sales execution.
Sales meetings and presentations
At some point every seller has sat in a meeting and wished they’d been just that little bit better informed. Being able to marshal exactly the right facts at the right time can sometimes make the difference between success and failure – augmenting that sales call by throwing in a useful or insightful contextual anecdote at the vital moment.
Or capturing and holding the attention of the room by having a constant supply of precise and up-to-the-minute insight about everyone sitting around the table – their sentiments, where they are in the sales cycle, and their expectations.
Advances in the performance and accuracy of AI technologies present many new opportunities to augment real-world activities with virtual sophistication. The right decision, the right action, the right delivery – to the right people at exactly the right time.
AI and MBL techniques will not only identify relevant news content, but correlate patterns of sharing with customers and prospects, along with actual open rates to predict how likely it is that an individual will be interested in receiving similar content.
Such assemblage of AI and behavioural learning mechanisms make it possible that software could completely automate such tasks – not only find interesting content, but automatically share it with the most relevant prospects and customers immediately.
Act on new opportunities
AI and intelligent software will make it easier to spot new opportunities – for example, to identify an insight trigger such a news story about a customer which may suggest they would be receptive to an approach.
The software will understand the context of the insight and then direct the best course of action, whether it’s a timely phone call, or the delivery of information about a product or service to provide a solution for their answers problem or need at that exact moment.
Improving sales behaviours
AI and MBL concepts provide an ideal set of techniques with which to analyse and model the actions and outcomes of seller behaviours, in order to pinpoint those which are most successful.
Likewise, the same approach can identify the opposite actions or inactions that typically correlate with failure. This information can then be used to facilitate coaching or suggest course-corrections – particularly useful for underperforming sellers.
Predicting customer needs
Predictive analytics open up the opportunity to not only make decisions quicker and perform tasks faster, but to understand where the customer journey will go next.
Furthermore, utilising machine learning will enable the construction of predictive models from data insight. As a result sellers will know the actions they can take in order to influence buying decisions now, next week and in the months to come, and from this build more sophisticated long-term sales strategies.
Garnering value from data
Data intelligence/business intelligence – it’s all data – a collection of facts (numbers, words, measurements, observations, etc.) translated into a form that computers can process. It doesn’t matter what you call it, or how you define it. What really counts is how sellers and sales teams are using it.
Data technology and artificial intelligence are rapidly becoming critical tools for sales success – translating data into insight, and enabling insight to be turned into actions that deliver improved customer experiences and ultimately more business.
Whatever you call, it in today’s world data is insight, and insight is power. It has never been more important for sellers to harness it effectively. Any business not thinking about how to harness the value of data and insight today may find itself losing out to competitors, and may ultimately become obsolete.
So I leave you with this – insight-driven businesses using more advanced technologies will steal $1.2 trillion per annum from their less-informed peers by 2020!
Data Intelligence/Business Intelligence/Artificial intelligence – we do it all, and we call it the Artesian of the Possible.