Machine learning is known to do cool and complex things. Businesses nowadays, like to find their exact target audience and offer their products or services to that particular group instead of investing the precious resources in a generalized group.
Wondering how machine learning algorithms work and what problems they can solve?
Check it out here:
What a typical ML algorithm does for qualifying a lead?
- Scoring for demographic
- Lead analysis
- Lead classification
- Behaviour analysis
- Forwarding for the next ‘manual’ action
- Using the data for enhancing the calculator functions
Whenever a new lead appears in the database, it is stored on the basis of previous training data and classification metrics.
On the basis of its score, it is calculated by the algorithm where a lead is ‘valuable’ or not.
If the lead scores below the qualification score, it is discarded for future processes. Otherwise, the machine learning algorithm waits for the next action of lead.
Once the lead reverts or takes some action, the sales threshold is counted. This calculation is done on the basis of revert time, link clicks, downloads, website visits, time spent on the website, and other such details.
If a lead qualifies as per the ML algorithm by crossing the benchmark sales threshold, it could be used by the sales representatives for the targeted actions. For example, it could be put in the ‘calling list’ of sales manager or ‘to meet’ list of your representative.
This final data or outcome of the whole process is re-utilized in training the sales threshold counting function and demographic counting function. By doing this, the continual refinement of the algorithm is done.
Method of database generation for lead analysis – analyzing market leaders
The above-stated algorithm runs by picking the data from a lead database. To create that list of leads’ data, multiple methods are being used in the industry. After the analysis of a few industrial tools, here are a few ways to provide input to ML software or apps, which engage or find out the right customers:
- Contact database creation
- Automated e-mailing
- Link clicks
- Open rate
- Chatbots and chat histories
- Technology stack analysis
- Website pixel trackers
- Customer behaviour
- What’s the best way to approach these prospects?
- What should be the channel of marketing?
- What is the most appropriate time to approach these prospects?
- What is kind of content is best for marketing?
- Quality and user experience analysis
Growlabs lets the businesses define what the ‘ideal customer’ means to you. Users can fill in the industries, company names, technologies the potential customers are using and more such details. Later, they render the list of most accurate matches from the 300+ million contracts, they have. As the leads are verified and the contact details are dynamically updated, it works efficiently.
The automated e-mails are sent and tracked by machine learning algorithms and tools, like prospect. On the basis of the status of previous e-mails, new ones are sent. For example, the first e-mail was opened by the lead and a link for service ‘online marketing’ was clicked. Now, the next e-mail will be shared targeting their online marketing requirements, as it is more likely to nurture the leads. The main matrices being tracked are:
Techechelons are experts at developing advanced chatbots. The conversations and conversation histories are tracked according to the region, occurrence frequency, text strings and more. And if some is talking second or third time, or looking more interested due to some keywords, the ML algorithm tries to ask contact detail and save them for you to contact later.
Some machine learning tools analyze the multiple websites and find out the ones, which are using the same technologies as your company is targeting. So, if you are in web development and designing, it can solve many issues.
Suppose – a visitor landed on a company website to check out HR software and services (as per the example). The visitor checks the homepage but leaves the website without checking business offerings. With machine learning, you can identify such a group of visitors, and separate them from your target audience because those visitors came for miscellaneous reasons, but buying services.
If a visitor checks the entire website and offerings, that’s the prospect! What machine learning will do is – it will identify these prospects and approach them on various marketing channels. Software with machine learning will also make itself learn various key points for each marketing channel. For example:
And many more questions like these.
It can also analyze what are the points or problems that your visitors faced during each session. Like, 5 out of 10 visitors got lost at a pop-up at the homepage or slow website speed or anything. You can fix these issues based on exact data and analysis.
Implementing whole automation through ML
The AI algorithms, like growbots, use ML for demand generation. Such machine learning algorithms search large databases for creating an automated mailing list and execute the lead filtering algorithm to determine which leads are valuable. Afterward, it runs campaigns, optimizes the outcomes and handles other inbox tasks to filter the best prospects. So, this approach could be tried too.
After-sale – using the data for predictive analysis
By analyzing the data and responses, machine learning increases your chances to get more customers. After a certain period of time, you can figure out what strategies to use to get the right customers.
Machine learning can also help your business in after-sale processes. The technology will answer these questions and help you make the right strategy to retain your customers:
- What are the customers that will continue with you for another year or more?
- What group of customers will help you raise your revenue?
- What are the customers where you can upsell or cross-sell?
- What are the customers that can increase my website traffic?
- What is the most effective channel for content promotion?
- What kind of content is liked by the prospects most?
Data mining is a hard process, and it becomes harder when you have a large customer base. Narrowing down your target list and reducing the efforts needed to convert the customer, it is emerging as a remarkable technology to help businesses find the right customers, and increase the business revenue. To come to a certain conclusion for customer acquisition or marketing strategy, machine learning can be a helpful technology. Though, it is equally important that you deploy the right machine learning tools and strategies to make it work with all efficiency.