How Businesses Use Artificial Intelligence to Improve Sales
Success of sales teams depends on its understanding of customer’s needs and building marketing campaigns around it. In these days of highly competitive and rapidly evolving markets a successful modern enterprise should proactively evolve sales campaigns that resonate with end users. As businesses move towards being data driven, marketing and sales has been the major beneficiary.
Why data is important for sales
With most activities being monitored and measured no other department generates as much data as the sales and marketing department. Sales teams have relayed on data from sales figures, customer data, foot falls and other parameters to understand, How the products are perceived? How loyal are the customers? or What is the foot fall to purchase conversion rate, to measure the success of the sales and marketing efforts. It makes sense to harness valuable insights from sales data to set realistic sales targets and forecast future demands.
How data analytics can boost sales
A good data analytics tool is vital , to garner helpful insights from sales data, marketing metrics and trends to forecast future demand and performance. These insights help businesses reach sales goals, meet customers ‘demands, enhance customer interaction/experience, optimize resources and innovate to meet changing markets ultimately drive sales and growth.
Some of the key benefits of sales data analytics include:
Improved lead generation
Identification of prospective customers has been done using customer data and market analysis information using traditional tools which most often do not bring out nuances of market demands. However data analytics has made the lead generation more accurate and less time consuming with the added advantage of automation of presales activities.
Data analytics applies lead-scoring algorithms and predictive analytics on the conventional data sources like customer’s previous history, current trends based on news reports or social media to identify better lead conversion factors. Sales analytics helps the sales teams in identifying highly probable customers to prioritize their efforts , understand if company’s offering meets customer needs and even identify member of purchase team who can influence the purchase decision. In addition, data analytics can identify the right time to pitch to customers for effective sales conversion. All these in turn guide companies on better marketing and sales strategy to boost sales and improve bottom lines.
Enhanced customer interaction
Sales teams often make cold calls in the bid to generate new business but poor conversion rates, result in wasted time and efforts. Appropriate data on past communications and customer preference will go a long way in improving customer response. Predictive analytics not only provide historical data but also insights on past interactions and length of engagement that help companies to devise successful sales strategy that reduces administrative burden and increase prospect contact time
Long gone are the days of one size fits all marketing strategies that results in wasted efforts, time and expenses. Customer segmentation aids implementation of focused marketing strategies to cater to consumers with different needs, shopping habits, interests and budgets based on their demographics. Marketing teams have successfully used data analytics to spot segments of potential customer with highest likelihood of purchasing.
Data analytics has been even used to optimise sales resource allocation. Personnel data combined with sales data has been utilised to implement better hiring, and training processes and identifying traits for improved field performance and behaviour. Companies can achieve improved sales by allocating high performance teams with important accounts.
Companies often resort to dynamic pricing to close a deal, but it is seldom easy to arrive at a right price as sales teams rely only on their experience. Sales analytics provides transparency regarding elbow room available for the teams while resolving trade-offs during negotiations and helps make decisions based on previous purchases.
Another challenge sales people face is pricing new products or services, with no preceding products or transitions to rely on. Data analytics have helped sales teams by implementing pricing algorithms that integrate real-time market and sales data with sales strategies to arrive at optimum prices
Improved customer retention
Since retaining existing customers is more cost effective than acquiring newer customers, increasing customer lifetime value of existing customers becomes top priority which is also a great way to drive sales.
Multi product companies most often manufacture related products or services which can be marketed together, but sales teams often fail to do so resulting in loss of sales. Sales analytics, implements next-related-product algorithms that identify patterns to cross-sell products and services to their existing customers strengthening customer engagement. This can be used to device customer specific sales strategies.
Companies put in a lot of efforts in retaining customers, despite the best efforts companies do lose to competitors. In order to retain customers companies need to recognise of sign of discontent in the early stages. Predictive analytics uses pattern recognising algorithms so that marketing teams can be forewarned to take appropriate action that ensure continued customer loyalty. Machine learning capabilities of AI can even use information gained from customers who are lost to competitors to fine tune algorithms that can flag buying patterns of unsatisfied customers.
Measure of marketing outreach effort
Even though success of marketing and sales teams is reflected in sales growth, it is vital to put matrices in place for periodic monitoring of effectiveness of product campaigns and sales strategies. Historically this is assessed through customer feedback and peer reviews, which are at best inaccurate and subjective.
Data analytics have automated the system of review, activity tracking and assessment so that checks and balances can be put in place to evaluate success of sales calls, marketing promotions and even success of sales teams.
Innovation and Disruption
Traditionally marketing teams have relied on customer feedback, surveys and other data gathering tools to gauge success of sales effort and marketing programmes. Data analytics have been used to harness this huge amount of data to garner insights to not only improve product quality but also identify opportunities to innovate and develop newer products. It can also help companies to remain agile in responding to the changing market by adopting quick turnaround times in both innovating products and customer communication.
Using data analysis companies have redefined the very core of their business and have been successful in bringing disruptive technology into major industry sectors including marketing and sales approach.
In the current customer centric environment to stay competitive companies should harness data analytics to predict customer behaviour and future market trends.
As companies strive towards being data centric they have to step up adaptation of data to gain insights that power changes. Data should be the driving force to market domination in this new world and we can help you in the process.