Working with Sales or Marketing campaigns day in and day out can be a draining, sometimes monotonous, job. But with Machine Learning (ML), it does not have to be.
Imagine a workflow where your sales team spends more time closing deals and less time chasing down leads that eventually don't materialize, and where your marketing strategies produce leads that virtually convert themselves. In this way, ML can revolutionize your sales and marketing practices while needing the least amount of work on your part.
A whopping 84% of marketing leaders reported using AI in 2020, an enormous jump from just 29% in 2018. This explosive growth within a span of just two years signifies a fundamental shift in how businesses approach customer engagement with the support of this technology. It is all the more relevant in the present day too.
According to 41% of marketers, AI and machine learning constitute their largest contributions to propelling revenue growth and improving performance.
But how exactly does ML work and unfold its impact in Sales and Marketing? Let's explore further into the topic and discuss the ways it can drastically improve your sales and marketing efforts, boosting your return on investment (ROI) like never before.
What can ML do for you?
At its core, ML is a branch of artificial intelligence (AI) that allows computers to learn from data without explicit programming. In the context of sales and marketing, this means analyzing vast amounts of customer data (e.g. purchase patterns, user demographics, online behavior) to identify repeating patterns and trends.
And just like that; your ideal customer and where to locate them is revealed right there.
With this information, you can:
Target the Right Audience
ML algorithms can segment your customer base into specific groups categorized according to similar characteristics and interests. This segmentation facilitates you to tailor your marketing messages and campaigns for maximum impact, ensuring that the right messaging is reaching the right audience at the right time. Your campaigns reaching the desired target audience have much more chances of delivering positive results as compared to campaigns being run by a trial and error approach.
Personalize the Customer Journey
Let's consider a scenario. A user browses your website for a specific type of waterproof jacket but does not make a purchase.
Given the conventional approach, the user might receive generic marketing messages promoting all sorts of products (or clothing articles in this case). With ML personalization, the tech can identify the user’s interest in the waterproof jacket by analyzing the browsing history and past purchase data if accessible. This calls for a more targeted approach.
The sales/marketing team can reach out to the user with a curated selection of waterproof jackets based on their previous online behavior, with suggestions included for similar products like raincoats, hiking boots, blankets, camping gear etc. They can activate website retargeting campaigns to display targeted ads for waterproof jackets on other websites the user visits. If the user added the jacket to their cart but exited before completing the purchase, they can be sent a personalized reminder (maybe with an exclusive discount code or free shipping).
Not only does ML offer a more personalized and engaging experience for the customer, but this also increases chances of purchase and builds stronger customer relationships.
Retain Customers Proactively
Customer churn can be a major blow to a business. But what if there was a way to prevent customers from churning?
ML can analyze customer behavior patterns to identify customers at risk of churning. This could involve factors like less frequent purchases, increase in complaints, or browsing patterns that indicate a search for competitor products. Identifying customers at risk of churning before they actually churn allows businesses to implement proactive retention strategies. For example, they could offer personalized discounts, loyalty rewards, or exclusive product recommendations to incentivize continued engagement, and thus reduce risk of churn.
Implement Dynamic Pricing
In a competitive market, pricing strategy matters. It can literally be a make or break factor for customers evaluating your product. ML can analyze customer behavior, competitor pricing, and market trends to recommend optimal pricing for your products.
Let’s consider a scenario. An online retailer sells noise-cancellation headphones with varying features and price points. Traditionally, the retailer might set a fixed price for each headphone model. However, with ML’s dynamic pricing, the retailer can tailor prices based on several key factors like demand fluctuations, customer segmentation, competitor tracking, and limited time promotions and flash sales.
With dynamic pricing powered by ML, the retailer can achieve increased revenue, higher customer satisfaction, and stay competitive in a dynamic market. Dynamic pricing driven by ML isn't just about raising or lowering prices; it's about adjusting your strategy to get the most value for both your business and your customers.
Take a Look: ML is All Around Us…
Spotify
Machine learning is deeply ingrained into Spotify’s business. The music streaming service uses ML algorithms to analyze listening habits, serve ads, and develop personalized playlists and suggestions for users. This not only helps users discover new music tailored to their interests, but this level of personalization also keeps them engaged within the Spotify platform. The campaign “Spotify Wrapped” that summarized a user’s listening preferences over the year gained vast popularity due it being closely personalized for each user based on their individual listening behavior.
Uber
Uber, a popular cab service, makes use of ML in several ways.
- Dynamic Pricing: ML algorithms analyze factors like demand, traffic patterns, and weather to adjust ride prices in real-time, ensuring fair pricing for riders and also maximizing revenue for the company.
- Route Optimization: ML predicts traffic congestion and suggests optimal routes for drivers, minimizing travel time and improving customer experience.
- Matching Riders & Drivers: ML algorithms match riders with the closest available driver, optimizing wait times and ensuring a smoother experience for both parties without long wait times.
Walmart
This well known retail store is using ML to improve their supply chain management, using it to track inventory levels, estimate consumer demand, and to optimize routes. This translates to fewer stockouts, faster deliveries, and overall, a smoother and more satisfying shopping experience for their customers.
An article by VentureBeat discusses how AI and ML are embedded everywhere at Walmart.
This innovative approach positions Walmart at the forefront of the retail industry, showcasing the immense potential of ML to transform how businesses operate and how customers shop.
A Roadmap to Sales & Marketing Success
The benefits of Machine Learning (ML) for sales and marketing are immense, but the initial steps can feel overwhelming. Don’t worry though, implementing ML is not that hard. Here's a practical roadmap to get you started and and access the potential of ML to boost your ROI:
1. Define Your Goals
To get started with ML, note down your specific goals. Do you aim to increase website traffic, increase the number of leads, personalize the user experience, or develop more optimized marketing campaigns to reach a more relevant audience?
Having well-defined goals ensures your ML implementation is targeted and provides a benchmark to measure success later on.
2. Identify Your Data
ML thrives on data. This is your raw material, the fuel for the ML engine. Start by determining what data you have readily available, such as customer purchase history, website traffic analytics, email campaign responses, and CRM data.
The more relevant data you can gather, the more accurate and effective your ML models will be.
3. Start Small
Good things take time! Begin with a pilot project focusing on a specific area like lead generation or campaign optimization. Try different approaches. See what works and what doesn’t.
This allows you to experiment, learn from the results, and refine your approach before scaling up your ML efforts.
4. Continuous Improvement
ML is an ongoing process of learning and refinement. Don't expect overnight success. The key is to analyze results, see what areas can be further refined, and constantly iterate on your ML models.
Developing a continuous improvement mindset can help you ensure your ML strategies stay relevant and are meeting your goals.
Next Steps
Using ML for sales and marketing efforts can truly transform your business. By uncovering insights into your customers’ preferences and implementing personalized strategies, your business can enjoy higher ROI and improved customer satisfaction.
Not sure where to start? Reach out to us and we’ll take care of it.
