Can a machine feel your customer’s pain?
“I feel your pain” – such an incredibly human thing to say, an expression of empathy and understanding.
For B2B sellers, empathy is fundamental to understanding the pain felt by prospects and customers, but if you want to solve pain points then waiting for that monthly catch up to find out what issues they are facing and what’s challenges are on the horizon is far too late.
Of course sellers harness social media, and are all too aware of the importance of social listening in order to pick up on sentiments and triggers that identify pain points, or a problems to solve.
But if customers and prospects are putting this information out there, then competitors are also reaching out for it – again too late.
Sellers that proactively investigate customer pain points well in advance have an instant edge, and an opportunity to seek first mover advantage by delivering proactive rather than reactive solutions.
With the rise of artificial intelligence – predictive analytics, machine learning and natural language processing – a seller’s ability to identify pain points has never been more optimised. But can a machine really feel customer pain?
Human understanding v machine analysis
As a professional working for an AI-powered sales intelligence vendor, of course I am a proponent of AI, but playing devil’s advocate for a moment, I can see why there might be hesitation when it comes to the benefits of using a machine to understand customer and prospect pain.
I recently spotted a blog post that quoted Martin Kihn, Research Vice President at Gartner saying “Humans are not machines, and the customer is always right. Too often, the algorithm gets that wrong”.
Truly understanding customers, getting to know their persona, views and opinions; and being empathetic about what’s keeping them up at night is a very human-based process – it requires interaction. So how can a machine possibly do this better than a human seller?
Getting back to basics for a second – the days when uncovering customers pain, needs and expectations was driven by past interactions, accumulated knowledge and gut instinct are far behind us – we live in the age of data and it is now the driving force behind modern sales strategies.
Humans simply don’t have the time, or mental capacity, to sift through the millions of articles, blogs, press releases, financial reports and social media feeds posted worldwide every day.
Instead sellers must turn to machines. Artificial intelligence for sales software that uses advanced analytics and machine learning to dig deeper into data sets and analyse, filter and present the actionable insights of greatest value – those that improve real-time understanding of the customer, their pain and what’s driving them at any given moment,
Examples of customer pain points:
- pain within their own organisation (redundancies, mergers, acquisitions, new growth targets, expansion into new markets, or financial pressures)
- pain points in the wider market (an new entrant in the market, a shift in customer dynamic affecting supply and demand, or new legislation/regulatory changes that could potentially impact their business)
- other external pressures such as supply chain issues.
The distinct advantage that machine has over human when it comes to understanding pain, and delivering opportunities to reach out and provide solutions, is that simply by crunching a vast amount of data it can uncover the underlying reasons behind pain, things that a customer or prospect themselves may not bring to the surface in the course regular engagements with a seller.
Of course I am not suggesting for a second that a machine can replicate human emotion and empathy (not yet anyway!), but customer pain point analysis offers an additional significant advantage – one that B2B sellers would be wise to harness sooner rather than later.
According to a recent McKinsey report, companies that have invested in tools to advance their understanding of customers have already started to pull ahead of their peers in terms of revenue growth (registering 2.3 times the industry average), profitability (3 to 5 percent additional return on sales) and shareholder value (8 percent higher total return to shareholders).
Man and machine working together – not that painful
Perhaps the question should not be ‘can a machine feel customer pain?’, or ‘how can a machine replicate human traits such as empathy?’, but rather ‘how can man and machine work together to improve the understanding of customer pain and deliver better outcomes’?
Investing in tools that make the sellers life easier and augment their own abilities is the aim – not just giving them data, but providing the right data, at the right time, and empowering them to take action.
The promise of AI for sellers is that it saves time and money while learning customer habits, preferences and behaviours for improved relationships, and uncovering and understanding pain points and underlying problems to be solved for smarter, more responsive and more confident sales decisions.
When man and machine work together its reduces the likelihood that sellers miss valuable business moments or engagement opportunities, fail to respond to customer pain, or fail to spot a emerging or market challenge or external pressure point.
How machines can help identify and solve customer and prospect pain
- Predictive analytics furnish sellers with rich customer data and machine learning algorithms remove the guess work when it comes uncovering pain and the underlying causes, ensuring that sellers can proactively respond with pin point relevance, timing, contextual awareness, empathy and personalisation.
- Advanced analytics assist sellers in building granular customer and prospect profiles augmented with relevant external data such as news reports, public financial information, and social media to generate a truly 360-degree view of each customer. Once sellers have such a holistic view they are not only better at understanding customer pain, but are also better equipped to know how to respond, and what actions will have the biggest impact for that particular customer and that exact moment in time. Furthermore sellers can empathise to a greater degree by having a deeper understanding of the motivations and sentiments of the people involved.
- Machine learning enables sellers to construct predictive models based on patterns of event types and customer attributes. As time goes by and the volume of data improves precision and predictive capacity, these models will advance further, enabling faster and more accurate predictions of customer needs, pain, market challenges and opportunities – before customers themselves even realise what lies ahead.
For B2B sellers, empathy is fundamental to understanding the pain points felt by prospects and customers, but building a truly deep understanding of pain and the underlying reasons for it is an incredibly complex task – customer pain points are as diverse and varied as customers and prospects themselves.
It’s about more than just sellers simply putting themselves in the shoes of the customers and prospects, but about learning, understanding and even predicting the pain felt by customers and prospects by analysing it from every conceivable angle – from the highest level of leadership down to the challenges felt by workers at every level within the organisation; from end-customers, from partners and every link in the supply chain, from market pressures, competitors and other external forces.
To put this complexity into context with financials with an example taken from Aberdeen – an average seller spends 14% of their time looking at customer data.
For a sales team of 200 where each member of which is earning an average of £50k per year, looking at data incurs £1.4 million of unnecessary expenditure per year.
By harnessing machines to understand customer pain sellers can augment those all-important human-based interactions with rich data-based understanding.
Not only will they become better and more efficient at identifying pain, understanding and empathising with it, but they will be better positioned to deliver solutions to customer and prospects’ problems.
Perhaps even more importantly for competitive advantage, they will become better predictors of pain, highlighting pain points customers and prospects could experience in the future, ones that they are not already aware of and providing a proactive solutions.
98% of people have the ability to empathise, but few of us use our empathy to full potential. Perhaps with the help of machines sellers can reach their potential, and when they say to a customer “I feel your pain”, they’ll be confident that they really do, and more importantly they’ll be confident they have a solution.