The pandemic was a rude awakening to the fact that the world of business is volatile and ambiguous. The Covid pandemic caused a massive drop in consumer spending, and the ensuing lockdowns disrupted supply chains globally. 2019 and 2020 brought about a period of a downturn that the world is still recovering from.
However, a surprising fact stated by a BCG study, is that during every downturn in history, one in seven companies actually managed to thrive. In contrast, most companies experienced a decrease in growth and profits; 14% of companies experienced an increase in sales growth rates (by 14%) and profit margins (by 7%). This, interestingly, held true even during the Covid pandemic.
The Boston Consulting Group (BCG) attributed these companies’ success to resilience. But what allowed these companies to stay resilient in the face of this particular downturn and grow despite an economic deceleration was technology. Companies that had already adopted digitalization or were quick to do so when the pandemic hit managed to thrive despite it.
A report by Finance Online stated that 70% of organizations have a digital transformation strategy or are working on one as of 2019. This number has no doubt risen now in 2024.
The only issue with this stat is the definition of ‘Digital Transformation’.
DX, or Digital Transformation, is the integration of digital technologies in different areas of the business to improve current operations and processes, or create new ones, to deliver more value to customers.
The term integration of digital technologies is extremely broad and encapsulates even basic forms of digitization.
This isn’t a bad thing. Adopting digital methods even at a basic level is a step forward in digital transformation. Still, a business that isn’t exploring the latest in technology is swapping one rusty method for a slightly less rusty one.
And that’s where AI (Artificial Intelligence) comes in. AI enables digital machines to go a step further – to learn, understand, and take action. It gives the digital platform a mind. An analytics solution, for example, is a digital method of tracking and storing data. It tracks user data and presents reports that analysts can study to extract information.
Armed with AI, however, the analytics solution can study the data and present insights itself, create an actionable plan based on these insights, and even execute these steps if automated to do so.
Many companies have begun their journey towards incorporating Artificial Intelligence solutions as a part of their digital transformation journey, but many are still oblivious to AI’s capabilities.
In this article, we will be exploring how AI can impact and improve business conversion rates.
1 – Delivering Excellent Customer Experience & Fast Service
No business is oblivious to the importance of delivering a stellar customer experience and its impact on the conversion rate. Many businesses, however, are unaware of how much of an impact it has. A survey of over 6,700 consumers and buyers globally by Salesforce found that:
- 80% of customers value their experience with the company to be as important as the service or product itself.
- 57% of customers switched from one brand to a competitor because the competitor delivered a better experience.
- 70% of customers said they expect consistency in processes in terms of engagement and communication. They want the transition between departments to be seamless and continuous.
These numbers are incredible when you study them closely. While the first two speak to customer service as a whole, the third stat introduces a new aspect to CX that has become relevant since the widespread use of technology and social platforms – delivering an omnichannel experience.
Image Source: Salesforce
70% of customers want the business to behave as a single entity irrespective of who the customer makes contact with or the medium chosen. The customer expects seamless handoffs, even if they switch from Twitter to Instagram and then to the website chatbot, and is handed over from the sales team to the support team. The communication should continue and not restart.
In fact, an Invespcro survey found that businesses that deliver an omnichannel customer experience retain 89% of their customers on average. On the other side, companies with a weak omnichannel engagement strategy retain only 33% of their customers.
Apart from omnichannel engagement, customers expect relevance and a personalized experience. A study by Epsilon found that over 80% of customers are more likely to purchase from a site that delivers a personalized experience.
The question is, how do brands deliver all these demands in CX in an increasingly digital world. The answer is AI.
AI-based omnichannel platforms tie all your customer-facing channels, like social media chatbots, direct messages, the website chat window, WhatsApp, Email, etc., and onboard all conversations onto one place. AI becomes the first line of contact, and based on the conversation; it routes the user to the right team with the information needed for a human agent to continue the conversation.
Machine Learning, a subset of AI, is instrumental in delivering a personalized experience. AI-based recommender engines use machine learning to study user behavior (past and in real-time) to show users products they are most likely to be interested in. AI allows you to deliver dynamic content that changes based on the user’s interests.
Implementing AI enables businesses to deliver an excellent customer experience that is a key to increasing conversions based on real-world surveys.
2 – An AI-Powered Autoscaler Maintains Uptime & Performance For Great In-App Experience
Autoscaling is a cloud computing method where the computational resources or number of active servers (or both) are dynamically adjusted based on the load.
For example, depending on how much data and requests a web application is processing, the autoscaler will increase or decrease the RAM provisioned to ensure it has the memory to run optimally without wasting resources.
Another example, which applies especially in the context of E-Commerce, is depending on the number of website visitors online, the autoscaler will increase or decrease the number of servers behind a site or web app automatically to cater to the load.
Websites and web apps experience different loads through the course of the day and from one day to the next. A site could get peak traffic all through the day on Black Friday, which can drop to minimal at night once the sale ends.
Since cloud vendors charge on a pay-per-use model, businesses need to be careful about how much resources they use. Assigning too little will result in the app crashing, and the business will lose customers and sales, but assigning more than needed will end in unnecessary cloud expenses.
But when traffic and requirements are erratic, how can a business stay on top of assigned computational resources? Through AI-powered autoscalers.
Google stated that 53% of mobile website visitors abandon a site if it doesn’t load within three seconds.
Ensuring the app or site is delivering optimal performance is key to maintaining CX and steady conversions, and performance will depend on the computational resources available to the app/site.
An AI-powered autoscaler enables the app to run optimally while also ensuring redundant resources aren’t assigned in two ways –
- Monitoring demand and adjusting resources in real-time (automated reactive allocation)
- Preparing resources for use by predicting demand using machine learning (automated proactive allocation).
By automating the process of autoscaling, a business does not have to constantly monitor usage and manually adjust allocation. This is a much slower and taxing reactive method. AI allows you to automate and stay on top of both, reactive and proactive resource allocation.
3 – Lead Scoring and Prioritization For Focused Sales Processes
AI can help your sales team greatly improve operational efficiency, and consequently Conversation Rates. Lead scoring and prioritization is a desperately needed service for sales teams who have to work with a large number of leads and also a large number of communication channels.
One of the greatest advantages of using a chatbot is that it can have any number of concurrent conversations at the same time. A bot can service any number of customers over multiple channels simultaneously.
But a good number of these conversations will eventually need human intervention, whether it’s a sales, service, or support conversation. Humans can only have one conversation over one channel at a given point in time. This can quickly become a problem when you have a limited workforce and a huge number of leads/requests.
When the first point of contact, whether it’s a chatbot, an email or a social media bot, is AI-powered, it is capable of analyzing the responses, the intent, even the emotions to prequalify the lead. A lead who opens an email and clicks on a link is more interested than a lead who opens an email and closes it in a second. A customer who asks about a product and its pricing and gives their personal information is more valuable than a customer who only makes basic inquiries and bounces.
AI is capable of measuring these differences and Scoring Leads, so they are sorted by priority when assigned to the sales team. Your limited task force can now spend their time focusing on leads that are more likely to convert and then work down the list.
By analyzing customer interactions and engagement on different platforms, AI is able to predict the customer’s preferred mode of communication. With this information, the sales team can reach out to leads on channels they are most likely to respond positively to.
For instance, if your business relies heavily on hosting webinars to get customers, AI analytics can look at the levels of engagement of each lead individually and determine if they are likely to even attend the webinars in the first place using the data it’s collected.
4 – Predictive Forecasting & Analytics For Proactive Measures
Predictive forecasting is another incredible feature of AI that helps businesses across multiple departments. The warehouse and logistics teams can plan inventory management accurately; marketing teams can estimate the outcomes of campaigns, businesses can predict product demand. Sales teams can get an estimated measure of lead quality, finance teams can manage budgets and spend better, etc.
What is predictive forecasting? It is the capability of AI, through machine learning, to analyze patterns in data to predict events or outcomes. Predictive forecasting or predictive analytics is being used across fields like banking, healthcare, insurance, retail, etc.
In the context of eCommerce, predictive forecasting is being used in many ways – for accurate product procurement, to deliver personalized content, to predict demand and price fluctuations, etc..
Having these predictive insights gives you the advantage of taking proactive action. When you’re prepared for a user outcome, you can influence the chances of converting.
5 – AI-Powered Site Search & Hyper-Personalization
The faster your customers find what they need, the faster you make a sale. A survey by Forrester found that 45% of people in the US will abandon their online purchase if they can’t find a quick answer to their question.
It’s a popular UX principle to ensure the number of steps from customer intent to checkout should be about 3 steps and not more than 5. This is not surprising when you consider the fact that over 87% of US shoppers make impulse buys. With impulse buys, the longer you make the customer wait, the less interested they get in the product. Reducing the number of steps in the purchase journey is also a matter of good CX.
With all that being said, you can presume how disastrous it would be if a user has to spend time searching for products on a poorly performing search bar. Ensuring your customers find what they want, fast, can be done in two ways – an AI-powered search bar and through the personalization of content.
An AI-powered search uses machine learning to dynamically show the user the most relevant results based not only on their search terms but also on other factors like past interactions, location, the time of the day, etc. It is also capable of making product recommendations in case of unavailability or for a cross/up sell, which helps your customers find exactly what they need and helps you Increase Conversion Rates.
Dynamic content is not limited to search results but can be used across the site, delivering a hyperpersonal experience for each visitor. Again, the AI-powered content engine analyzes user data through machine learning to show the most relevant content dynamically.
Conclusion
We started this article by saying that most businesses are adopting digitalization, but most aren’t fully utilizing the powerful solutions available today.
Digital transformation is an ongoing process, but each step consumes time, effort, and money. Backtracking and changing solutions midway can be detrimental to your ROI and the prudent approach is to select the right technologies and solutions from the start through research.
All through the article, we emphasized the impact AI can have on conversion rates. AI takes digitization a step forward, and it should definitely be a part of your digital transformation strategy.
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