Artificial Intelligence is one of the most popular technology trends in 2020. The reason is simple: the innovative capabilities of AI development seem to be the number one choice among businesses. Especially sales automation is gaining momentum with AI integrations. The journey from lead generation to the buyer’s purchasing decision is now strategically planned with AI.
Let’s take an example of the StoreHub; it is a cloud-based retail management solution. The firm based in Malaysia needed a robust solution for its follow-up process. The whole follow-up process for any lead was manual, and that is where StoreHub was facing efficiency issues.
StoreHub took the help of automation tools to create a lead tracking system. It helped the firm increase conversion rates by 20%. If you are also looking for AI development strategies to improve your conversion rates, here are five effective ways to do it.
#1 AI development strategies
An AI development strategy is quintessential before developing AI-based software for your marketing campaigns. If you are looking to formulate an AI development strategy, here are your critical point of strategizing,
- Problem Detection- Do you need AI for detecting a problem?
- AI Recognition- Do you need to identify or recognize something with AI?
- Product/Process Classification- Do you want AI for the classification of products/processes?
- User Segmentation- Do you need AI development to have efficient segmentation of customers?
- Natural Language Processing- Do you want to gauge the sentiment of consumers or understand the language?
- Automated Recommendations- Do you want your consumers to have automated recommendations?
Answers to these questions will help you strategize AI development better. Now that you have a strategy to develop an AI engine let’s see how it can help you with conversions.
#2 Smart Traffic Analytics
Any marketing strategy is clueless without proper research on the buyer’s persona. A buyer’s persona is often a fictional or semi-fictional representation of consumers. It helps marketers to have comprehensive real data regarding the consumer.
Now many companies create a database of different personas. But, as most of the buyer’s persona is based on titles and positions, it needs constant updating. The process of updating the buyer’s person is not only expensive but also relatively inefficient. With Artificial Intelligence, updating of profiles and its transition to personalization becomes simple.
For example, a conventional marketing firm and AI company like MIQ(MarainalQ) has been given a list of 5000 companies to find people in charge of SEM(Search Engine Marketing). The conventional services came up with 55000 names, with 15% matching the criteria of the search. At the same time, the AI engine produced 20000 names with an accuracy of 81%.
Once you know the exact buyer’s persona, the next thing that comes to your mind is personalization. Content strategies personalized according to the buyer’s persona can help generate leads and even nurture them to reach the buying decision. So, let’s discuss how AI development can impact content strategies.
#3 AI for Content Strategies
Personalizing the content is not that easy. Especially if you are looking for better conversion rates, personalization of content can help you big time. But, the complexity of personalization is much more than you can fathom. AI can help you with content strategies and personalization. Here are some practical ways to do it.
An AI algorithm can help you create a content profile. It is an overview of all the content that you produce for consumers. Whether it’s blogs, articles, website content, or even social media, AI algorithms can track every bit of information.
Developing an AI ecosystem for content strategies can help improve feedback control. You can track traffic, preferences, and other metrics of content performance with AI development.
AI-Driven Content Creation
Artificial Intelligence and machine learning algorithms can help you choose the right topic for content creation. AI helps create better content ideas. These algorithms analyze the buying patterns of consumers and guide content creation for influencing decision making.
Now that you have the buyer’s persona and personalized content let’s see how AI helps with customer relations.
#4 AI for CRM
CRM or Customer Relationship Management is not an easy task these days. As the channels through which consumers can connect increases, CRM becomes complex. But, AI can help you achieve better results.
So, How can AI help with CRM?
Artificial intelligence can work as the aid of a salesperson. According to a report, an average salesperson uses 17% of the time to perform data entry and 21% on writing emails. With Artificial Intelligence, you can automate data entries and even email marketing. So, the salesperson can focus more on strategizing and nurturing the lead.
#5 AI For Decision-Making
The last stage of closing a sale is the purchasing decision. AI can help convert your leads into sales with a cognitive model of BigData, summarized data, human judgment, and business decisions.
A cognitive AI model will gather data from different sources like social media, emails, sales records, website traffic, mobile apps, and many more. A summary of these data is analyzed by an algorithm suggesting recommendations for human judgment. The influence of AI suggestions can help buyers make decisions in the direction of campaigners or marketers.
If you think about the future technology trends that may affect business decisions, AI is a front runner. From data sourcing to lead generation and from personalizing the content to nurturing conversion, AI can help you at everything. But, you need to formulate a reliable AI development strategy. Here, we have tried to answer some questions, and for further information, you can reach out to us from here.
Hardik Shah is a Tech Consultant at Simform, that provides best mobile app development services. He leads large scale mobility programs that cover platforms, solutions, governance, standardization, and best practices. Connect with him to discuss the best practices of enterprise application methodologies @hsshah