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The definition and concept of machine learning have drastically changed over time. Initially, machine learning had its interests set on recognizing simple data patterns. However, brick by brick, more advanced features were added, helping transform it into the complex system it is now.

Machine learning incorporates strings of information running on neural networks, which self-trains its system to recognize and interpret application such as text, images, and videos. The gradual developments have enabled tracking major information big data holds — through complex algorithm analysis. Industries of all scale and magnitudes are prospects to its vast range of features, which enables to better understand and perform designated tasks.

This blog dedicates its interest in the correlation of machine learning with sales and marketing. We focus on areas machine learning has been aiding each field in enhancing performance with consumer-friendly strategies.

Machine learning provides value to enterprise data and soaring profits. In the advanced world of big data, machine learning is becoming efficient at scouting prospects with higher predictive accuracy because of its ability to break down complex data. Machine learning is efficient at handling predictive tasks including – defining which behaviors have the highest propensity to drive desired sales and marketing outcomes. Businesses eager to compete and win more customers are applying machine learning to sales and marketing challenges first.

In the MIT Slogan Management Review article, Sale Gets a Machine Learning Makeover, Accenture Institute for High Performance shared the results of a survey comprising companies with $500 or more in sales targeting to increase sales using machine learning. Key takeaways from their study results include the following:

  • 76% say they are targeting higher sales growth with machine learning. Gaining greater predictive accuracy by creating and optimizing propensity models to guide up-sell and cross-sell is where machine learning is making contributions to omni channel selling strategies today.
  • At least 40% of companies surveyed are already using machine learning to improve sales and marketing performance. Two out of five companies have already implemented machine learning in sales and marketing.
  • 38% credited machine learning for improvements in sales performance metrics. Metrics the study tracked include new leads, upsells, and sales cycle times by a factor of 2 or more while another 41% created improvements by a factor of 5 or more.
  • Several European banks are increasing new product sales by 10% while reducing churn 20%. A recent McKinsey study found that a dozen European banks are replacing statistical modeling techniques with machine learning. The banks are also increasing customer satisfaction scores and customer lifetime value as well.

Prior to machine learning features like data mining, businesses relied on historical and static data which have now been converted into real-time data. Besides sales and marketing, machine learning has scope in every area that requires data mining and analysis.

1. Content Generation/Curation:

Creating accurate and interesting content is extremely important. As to which, marketers have utilized software tools which granulate contextualized content per the interest of targeted individuals. Being the primary gateway to customer engagement, marketers need to give heed to the type of messages they generate targeting specific demographic group or individuals. Such subject matters — reflecting brand purpose — can be relayed to the audience through digital online marketing platforms.
Amidst multiple platforms, social media have so far been the most popular engagement ground for businesses and individuals. The leading reason for its popularity can be credited to the contextual content for consumers of all demographic groups. With the help of the enormous amount of data present in social media platforms, interest graph cumulates preferences of different demographics aiding in the precision of content generation and curation.

2. Pricing Products:

Pricing a product is difficult — especially if you need to experiment a product of similar value with different prices. Product pricing can vary per the distribution channel length, caused mainly by the variation in the geographical location.
Reaching an optimal price range is important and machine learning can help in this regard. Below is an image of how machine learning helps.

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3. Predictive Customer Service:

Using machine learning in customer service is like putting intelligence into action, the possibilities are endless. It helps businesses plan for resources and personalized communication. One such AI that is helping data scientists predict customer expectations more accurately is Sales Force Einstein which is an A.I for CRM. Companies dealing with a large amount of structured and unstructured data, images are like to join forces with Sales Force Einstein for better customer service, empowering teams and delivering right answers.

4. Sales and Marketing Automation

Sales process is a linear process. It takes sales reps sheer agility to guide prospects, gracefully, down the sales path, convincing to convert – eventually into a customer. At present, there are AI tools with the intention of converting prospects but the use such automated voice calls lack productivity. Therefore, sales organizations have not drifted their priority from real-life sales reps to automated machines. However, sales and marketing tools have a drastic influence on the sales process. These tools guide the reps in finding leads and take the sales process to the next level. Numerous sales automation software such as Fusemachines have been developing platforms, which enables sales people to scout for leads using an automated platform. Such platform generates leads with much efficiency, using algorithms to search for contact details available on the internet.

5. Email Automation

Email automation enables effective engagement with customers because it gives you the privilege to choose your own unique designated time for sending emails. Automating emails allow businesses to spend more time crafting an email maintaining tone consistency and avoiding flaws to the maximum. Furthermore, use of key performance indicator lets businesses know the ratings of individual emails based on delivery, open rates, and clicks.