Here’s why machine learning is going to explode and tips on how you can prepare for it.
Marketing Success
Demand for machine learning and AI is growing. In 2018, according to Forbes 84% of marketing organizations were going to implement or expand their AI and machine learning. This pattern makes sense as 75% of those who have already implemented AI and machine learning have been able to enhance customer satisfaction by over 10%. As well, those using AI and machine learning have increased sales for their new products and services by over 10% for 3 out of 4 organizations.
The Customer Experience
Forbes also reported that 57% of executives feel that improving customer service and support is the greatest growth benefit of machine learning and 44% feel it will help improve existing products and services. The earliest adopters of machine learning have used it to attract more customers, improve their selling techniques and effectively serve their customers thanks to improved accuracy in predicting outcomes.
The two highest priorities for 58% of enterprises using machine learning are new product development and customer care. These “need to dos” pose the highest complexity while offering the highest benefit.
Areas that are considered “must dos” such as virtual assistants, chatbots and the reduction of revenue churn were less important despite offering high benefit with low complexity.
Dynamic Content
What seemed like futuristic possibilities yesterday are now sure to be a reality by 2020. This includes:
- Real-time personalized advertising across digital platforms
- Optimized message targeting accuracy
- Greatly improved context and precision targeting
These improvements will allow B2C companies and retailers to increase sales with improved Sales Qualified Lead (SQL) generation.
This will make it easier to predict the best sales offers, incentives and programs to show prospects and existing customers. Contextual content in hand with targeted offers and incentives generated by machine learning will help revolutionize marketing through accurate predictions perfectly designed to upsell, cross sell and encourage new sales. An example of this is Amazon’s product recommendation feature.
Improved Earnings
According to McKinsey Global Institute, with machine learning helping to improve demand forecasting, assortment efficiency and retail pricing retailers can see a possible increase of 2% improvement in Earnings Before Interest & Taxes, a 20% stock reduction and 2 million fewer annual product returns.
They also found that retailers that learn to use AI and machine learning effectively can improve value chain performance by as much as 50% in assortment efficiency and an increase in online sales of 30% by introducing dynamic pricing.
Lead Qualification
Lead scoring accuracy is improved using machine learning as part of the lead qualification process. It also allows marketers to trace their increased sales to their marketing campaigns and sales strategies.
Relevant data available through machine learning from the web creates predictive models to create profiles and personas of the ideal customer. This provides a predictive score for sales leads that will allow for better predictors for prospects and new sales. Sales teams can prioritize more effectively so their sales and selling strategies become more successful.
As well, sales projections can become more accurate based on customer segments and microsegments using machine learning dependent on recency, frequency and monetary modelling. Companies can better define their ideal customers based on loyalty, spending, almost lost customers, etc.
Impact on Digital Marketing
Machine learning is empowering marketing teams to optimize their marketing strategies by having access to easily understandable insights. They are better able to not only understand their customers, but also predict their needs and actions. This in turn allows them to optimize their interactions with customers for more successful outcomes.
Infographic courtesy of: Dataiku.
How to Prepare for Machine Learning
Marketers will have to embrace machine learning tools to automate processes and utilize data more effectively. You can look at the following opportunities machine learning has to offer:
Content marketing
Machine learning tools can help you zone in on what LinkedIn has found to be the three most important aspects of effective content:
- Audience relevance
- Engaging and compelling storytelling
- The ability to trigger an action or response
You can use machine learning to track consumer trends to find actionable insights that will generate more leads through highly targeted and persuasive content.
Pay per click campaigns
You can improve your pay per click (PPC) campaigns by accessing the metrics you need to optimize your success. Machine learning will help you make strategic decisions based on past performance so you can overcome the common struggles that can interfere with meeting your goals.
Search engine optimization (SEO)
Machine learning will help keep your web pages and other online content high in the ranks by helping address SEO algorithm changes across major search platforms. You can access what will be most effective in your searchable content to attract search engines. Machine learning can help you zone in on key information that will improve the quality of your content so you have an advantage over the competition still depending strictly on keywords.
Content management
Analyzing content, keywords and phrases takes time and can be ineffective when not managed properly. Machine learning can help drive brand awareness by improving your ability to create meaningful relationships. It will optimize engagement providing valuable insights that will improve content and also help you meet the desires of your customers and prospects.
Chatbots
These friendly little virtual robots have become more and more effective at handling initial conversations with customers. They can work through text and voice command and are the power behind Siri and Alexa. Facebook is developing chatbots for advertisers and many companies have chatbot ambassadors on their websites. Using natural-language processing (NLP) chatbots are hard to detect and can assist customers efficiently. They can not only carry on conversations, but also collect data to determine product preferences.
Photo: Freepik / Sompong_tom
As machine learning and AI become mainstream, digital marketers, retailers and sales teams will become more dependent on them to improve ROI, enhance customer service and ultimately positively affect their bottom line.