The world market share of the use of predictive analytics technology is constantly growing. Experts prove that by 2023 it is going to reach $56 billion, which is 5 times more than the current number. What makes this technology so lucrative for businesses all over the world? And how can companies from completely different industries and niches use it to boost their profits? Today predictive analytics professionals from Indatalabs software company help us clarify these questions.
Core Benefits of Implementing Predictive Analytics
In the general sense, the use of predictive analytics in sales helps to remove various human errors when analyzing data on goods trends, delivery options, customization, lacking features, etc. Plus it helps companies better understand their customers’ needs and tune their products and services according to the gained info. But let’s take a look at each benefit of using AI-based analytics in detail.
- You will get more qualitative leads
Having a huge database full of various leads but no clients is a common situation in too many companies from various industries. The reason is simple: a vague picture of the target audience, and as a result – poor leads quality, unstructured database, and inability to somehow use the gained results.
Predictive analytics helps to solve the core reason for lacking customers, as it draws a clear picture of what your target audience is, together with their needs, problems, and desires. With the help of AI-based instruments, marketing teams can compile precise ICPs (ideal customer profiles) and hit the needs of their leads, turning them into clients.
Using AI sales instruments also significantly saves time for your marketing specialists, as they don’t need to waste it on leads that don’t fit. This is an additional resources optimization bonus that will boost your profits together with prediction-based lead generation.
- You will be able to specify and tune your messages
This benefit is closely connected with the previous one: when you have precise ICPs with detailed buyer persona portraits, you can tailor your messages, making them more appealing to customers’ needs. You can describe in detail the problems your target audience faces every day, and show how your product or service can solve them.
You can also diversify your selling strategies, trying different approaches, and leaving those which got the best results. For example, with some leads it’s better to use a long-lasting communication strategy, nurturing them with useful info, and leaving enough time to think about your offer. The others, and vice versa, are ready for fast sales and willing to get the result as soon as possible. Predictive analytics helps to distinguish one from the other and create deeper relationships with leads and prospects.
- You’ll get better lifetime customer value (LCV)
When you’re a seller who understands people’s desires and helps them to cover their needs, they will more likely return to you for another purchase than to some unknown random seller. The level of customer satisfaction plays a vital role in the rate of LCV: the more satisfied clients you have, the higher percentage of them will become regular and recommend your services or products to other people.
AI-based analytics technology allows making your ads, messages, and services highly customized when the same product can fit a great number of people if just slightly changed or presented differently. Thus, people get the feeling they are seeking – that they’ve been treated personally and their special needs were covered. This boosts trust in your brand and not only increases your LCV but also cross-selling and upselling rates.
- You will easily launch new products
Launching a new product is always a thrilling and risky campaign, as it’s difficult to say whether the product will be popular among customers and when to wait for the first profit. Previously, sales managers had almost nothing else to do but collect a few random pieces of research and rely on their gut feeling in deciding which product to launch.
But with the rise of predictive analytics, this task has become much easier. Now sales managers have diversified info from current and potential customers on their preferences, needs, and coming trends, and can start selling novelties with confidence and even approximate numbers of profits gained at each stage.
Top 3 Universal Strategies on the Use of Predictive Analytics
Knowing the benefits which you can get with sales predictive analytics is good, but you can fully enjoy them only with practice in your sales department. That’s why below we’ve collected the top 3 universal examples of the use of AI-based predictive analytics in everyday and strategic marketing tasks, which can help you discover their full potential.
- Reading customers’ minds
AI-based analytics tools allow not only to structure the info gained on customers’ previous purchases and preferences but also make detailed predictions of what customers may need and when they are going to buy something next time. This is the ultimate strategy in selling, as it allows one to anticipate people’s desires and deliver the very thing they need at the right time.
This strategy is possible due to the use of ML (machine learning) technology. Using ML, you can tune the presentation of your goods and services in the marketplace according to a certain customer’s desires, and hit their needs without mistakes.
Source: Unsplash
- Decreasing clients’ churning rate
AI-based analytics tools give you lots of valuable insights into how clients see your product, what they like and dislike, how much time they spend looking at each product item, and when they decide to leave your online store. Understanding all these hidden processes will give you a clue why you lose clients, and you will be able to find the common traits among all the clients who are going to leave you.
When you find the root causes of why customers leave, you will be able to prevent this or at least change your strategy, which will be vital in building long-term relationships. Thus, AI tools help to quit using numerous strategies that don’t work and concentrate only on those that actually work.
- Leads prioritization and structuring
It’s a very common situation when the sales department spends its resources on all accounts at once, hoping that at least some of them will turn into clients. But the fact is that only a small part of all the accounts are really valuable, and if to concentrate on them, you’ll get better KPIs with fewer resources and time spent. Predictive analytics tools give you the opportunity of lead prioritization: it helps to define the client’s potential to buy, segment different leads, personalize messages, and even guess whether the client is ready for cross-sells.
Of course, in order to get all this information, you need to collect lots of data on customers, quickly analyze it and prioritize. AI-based sophisticated data mining methods used in predictive analytics are perfectly suited for this, helping you get the needed insights in a short time.
Wrapping Up
Gone are those days when making predictions about customers’ behavior was only possible for large eCommerce corporations due to their expensive and time-consuming field research. Nowadays the impact AI, Big Data, ML, and NLP technologies have on selling strategies has significantly grown, and making predictions is rather a necessity to compete with other brands than a luxurious feature.
Now every eCommerce company can afford to have robust AI-based tools, tailoring their strategies for better KPIs. With the right solution in hand, your marketplace or e-store can enjoy all AI-related benefits and quickly increase profit, customer satisfaction, retention, and other important marketing rates.
The only question is whether to build such a tool in-house or via a software vendor. We would rather recommend turning to AI specialists with a solid background in developing custom solutions for sales, as they will definitely help you to get the very solution you need in an approximately short time.