5 Ways Artificial Intelligence is Transforming CRM
It appears that 2019 is the year when the union between Artificial Intelligence and sales has finally evolved. While this relationship began to gain momentum last year, applications that married the two were limited. Recent research has shown that more than half of CRM users believe that the most important factor for a good CRM is ease of use. A CRM that isn’t easy to use simply won’t be used by employees. That is the reason why adoption rates among end users are still as low as 25%, despite growth in CRM purchases, according to Salesforce.
A report by Pactera Technologies and Nimdzi Insights found that 51% of enterprises leveraged a form of artificial intelligence in 2017, while the percentage increased only to 53% adoption in 2018. In 2019, artificial intelligence will play an increasingly vital role in sales organisations. The main impact will be found in the CRM arena. This year we’re sure to see a transformation that will magnify the capabilities and effectiveness of out-dated CRMs.
Here are 5 ways AI is transforming CRM:
1.Increased Automation
One of the main barriers to adoption have been manual CRM tasks. A lot of the time employees feel like they’re serving CRM, instead of it serving them. AI however, is now changing that.
According to Kate Leggett, VP and principal analyst for CRM and customer service at Forrester Research “AI and automation are the most pervasive trends impacting CRM and changing the nature of work”. She goes on to say, “What AI is doing is offloading the routine work from CRM users, and allowing them to focus on the harder work of nurturing relationships which strengthens retention and lifetime value.”
At present, sales professionals spend around 17% of their time undertaking data entry tasks, that amounts to almost a full workday per week. AI will not only empower these professional to eliminate data entry, but also enhance their ability to centralise disparate customer databases, leading to the opportunity to capture the complete customer lifecycle (regardless of channels: e-mail, chatbots, phone conversations, etc.)
For example, CallMiner Eureka uses machine language and artificial intelligence to capture and transcribe interactions with customers. These transcriptions are then tagged according to key topics and categorization schema. Employees can then search transcript metadata for keywords, phrases and even acoustics like amplified voice intonation that may mean excitement. This can lead to salespeople being better equipped to distinguish vital customer tendencies.
2.Sentiment analysis
One of the most important aspects is that salespeople develop higher levels of rapport and trust with customers. Salesforce conducted a research that found that 79% of buyers believe it’s very important to interact with a salesperson that’s considered to be a trusted advisor. However, there’s a long journey ahead as it is reported that merely 3% of buyers trust sales representatives.
Due to the fact that nowadays the majority of customer interactions occur virtually and body language and facial expressions are hidden, salespeople face the excruciatingly hard task of developing trust and rapport with customers. This is where sentiment analysis comes into play with tools like Cogito. For example, the tool provides in-call voice analysis that allows salespeople to better understand their customers’ emotional states. It does so by offering a color-coded meter to gauge how a conversation is going. So, the colour would change from green to yellow or red if a buyer or salesperson reacts too abruptly. The tool evaluates various key aspects of a conversation including empathy, participation, tone, pace, and interruption.
When historical in-call data is finally integrated with CRMs, the benefits could be far-reaching. Not only could they gauge conversations in real-time, but also leverage the output for training purposes in order to improve relationships and conversations with customers.
Joshua Feast, the CEO and Cofounder of Cogito said, “Conversations are like a dance…You can be in sync or out of sync.”
3.Data integrity
CRM data gets a new lease of life when AI comes into play. Currently it is known that CRMs are brimming with dirty data. A research by Dun & Bradstreet found that yearly 91% of data in CRM systems is incomplete, 18% is duplicated and 70% is rendered stale. This dirty data has a scarily devastating effect, with 80% of businesses believing that it disrupts their sales pipelines and 25% of them believing that bad data also leads to reputational damage.
Parsing data and spotting trends are two key areas AI is well-known for. CRM systems are now starting to take advantage of it. Companies such as Cien are combining AI with CRM for predictive lead generation, automatic trend spotting and parsing some of the deeper dynamics going on with contacts. AI tools are also integral to cleanliness of data. It is able to detect anomalies, duplicates, irregularities and other errors that affect CRM data, leading to compromised customer relationships. Therefore, by integrating with 3rd party databases, AI can update records and interpolate missing records in real-time as data changes.
Data cleansing has always been one of the main deterrents when it comes to salespeople embracing CRMs, but now AI seems to be the solution.
4.Predictive lead scoring and Sales process optimisation
Older CRMs aren’t very intuitive when it comes down to understanding what drives sales. It also lacks personalisation.
According to Vinay Ramani, chief product officer for Pipedrive, this is one of the main reasons that salespeople struggle with a CRM that is just a reporting tool for management and not a tactical weapon. He goes on to say “If a CRM isn’t showing you routes and driving efficiencies, then it’s not valuable.”
When AI is woven into CRM, it helps organisations drive sales by assisting in forecasting, upselling, cross-selling and price optimisation. Not only can an AI algorithm give sales departments ideal discount rates by reviewing past deals and characteristics, it can more accurately predict future quarterly revenues, and identify existing customers who are more open to updating past product purchases or buy new products altogether.
When it comes to predictive lead scoring, AI allows salespeople to boost their lead scoring abilities with algorithms and predictive analytics. It has been found that 96% of visitors that arrive to a company’s website aren’t ready to buy, so it isn’t a surprise that 74% of companies say their top priority is converting leads into sales.
Sales representatives will no longer have to rely on manually ranking leads, according to a set of rules, a strategy that is now outdated. Now, with the help of AI millions of different real-time and historical data, including geographic, activity, and demographic data, can be analysed to detect trends that can inform predictive lead scoring methods.
One of the most thrilling facts about predictive lead scoring tools is that they rely on a “champion-challenger” model. This means that various predictive models are verified and then the most accurate one is selected. Whenever a more precise model is found, it automatically becomes the default.
5.Improved Personalisation
Artificial intelligence also helps CRM deliver a far more personalised customer experience. Legget at Forrester says, “AI will lead the way into the next generation of personalized experiences by automated actions, predictive accuracy and customer journey.”
CRM coupled with AI is now starting to inform users of potential upsells, offer next best actions, help CRMs interface with marketing, support, and sales and spot customer care issues before they go beyond the point of no return.
It’s no secret that CRMs are in need of a refresher and the time for that is now. Gaining the trust of the customer is a feat without which sales reps can no longer survive. Thankfully, AI is here to equip salespeople with the tools to transform their customer relationships.
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