No-code AI allows smaller companies to access data-driven insights about their customers. Here’s how it can help with sales, marketing, and beyond.
The computers on your business’s networks hold significant amounts of data that AI technology can analyze for patterns. Those patterns reveal important insights into how well you're running your business.
Until recently, if you wanted to build a bespoke AI platform for your business to get at those insights, you would need to hire developers, programmers, and data scientists at considerable expense. But now, you can use no-code AI, making such data findings more accessible to businesses..
In this article, we examine:
What artificial intelligence and machine learning are
No-code AI and its benefits
12 exciting current uses of no-code AI apps
Deploying AI in your business
What Is Artificial Intelligence?
Artificial intelligence allows machines to simulate and mimic human decision making and use human-style thought patterns to solve complex problems.
Examples of AI already in everyday life include Alexa, Siri, and internet chatbots answering customer support and technical questions using feedback loops.
Those feedback loops tell them whether they have been successful or unsuccessful at a given task so that it’s likelier to perform the “right” action for the same task next time.
Machine Learning and Deep Learning
Machine learning (ML) and deep learning are technologies related to artificial intelligence.
While the goal of AI is to maximize the chances of delivering the desired outcome to a challenge, machine learning aims to accurately analyze data, often through pattern detection.
Machine learning models are designed to make decisions or predictions following analysis of (usually very large amounts of) historical data which may or may not be structured.
Everyday examples of machine learning we currently use include when Netflix suggests films we might like and Google Search suggesting relevant autofill options when you type a query into the search bar.
Deep learning (sometimes known as neural networks) is a mixture of ML and AI where the AI attempts to mimic human thought to extract specific types of knowledge from data.
The best known (and so far incomplete) deep learning project is probably Tesla's attempt to teach cars how to drive themselves.
Being able to deploy AI and ML within a business offers tremendous potential for greater efficiency, customer service, and profits. We’ll look at the possibilities this offers later.
But to put AI and ML to work in a company, you normally need someone who is good at writing code. That is, unless you use no-code AI.
What Is No-Code AI?
No-code AI solutions allow non-technical users to use automated code-writing which would otherwise require an experienced developer or computer programmer.
How do they work?
No-code platforms are similar. You drag-and-drop "functions" into the required order and the platform creates the code for you. You do not have to write a single line of code.
No-code tools allow business users to create many different types of apps, including:
Workflow management tools
Usability testing platforms
No-code AI is often used in companies with programmers and developers. In these situations, teams and departments often build their desired app using no-code AI tools. Programmers and developers later finesse and add extra functionality to the app.
There are drawbacks to using no-code development tools, however.
Because each function is preprogrammed, it can be difficult to create highly customized AI projects if you don't have a developer or programmer to plug complex and missing gaps in the process.
Many no-code solutions are also paid-for modules. Depending on the policies of the developers, they may include code in their apps that prevents them from interacting with functions contained in apps from other vendors.
Business Use Cases for AI
The process of building AI applications and software development is much faster with no-code AI than it is with traditional coding and even low-code AI.
But what can AI do to improve efficiency and profitability? Let's have a look at some of the current no-code AI and ML functions on the market.
Thanks to functions available on AI and machine learning platform marketplaces, companies can see in real-time how good incoming leads are, how likely customers are to stay with them, and how much revenue they'll get from deals in their pipeline.
Simplify Lead Quality Scoring
You can connect a lead quality scoring AI function to your version of Salesforce. You can then interrogate your current customer database and then assign a score to each new inquiry based on variables like educational status, marital status, job, and age.
Now, your sales team can better target leads and look more closely at which ones are most likely to convert.
Reduce Customer Churn
Churn is a particularly important metric for, in particular, software-as-a-service companies. Churn is the proportion of people and companies which stop their subscriptions and switch to a new provider.
AI can identify the customers whose demographics and user patterns suggest a higher likelihood of churn. You can then target offers to those users to encourage them to stay with your company.
Predict Time to Close
Sales managers check their team's current level of expected revenue as well as individual rep performances by examining their pipelines. Pipelines contain details of how far along live prospects are in the sales process and how much they're likely to spend.
There are now AI/ML apps that allow sales managers and reps to more accurately predict how long it will take before deals in the pipeline finally close based upon historical datasets.
Predict Deal Size
As well as predicting when sales will arrive, AI apps can predict the likely deal size from a prospect.
As well as giving greater foresight into future revenues, sales teams can now choose to spend more time on bigger deals instead of getting themselves tied up with small ones.
For any sales team to function properly, their co-workers in marketing need to provide them with a steady stream of high-quality and actionable leads. How can AI and ML help here?
Sales Funnel Optimization
A sales funnel is designed to capture people's contact details at different points of their buying journey from initial awareness that they might need or want a product to the point where they're ready to buy.
Sales funnel optimization AI apps allow marketing teams to analyze a variety of factors including where a lead has come from, the number of visitors to a website, what they've done or looked at when at your website, and how long they've spent exposed to sales funnel content.
These apps will tell your marketing teams the parts of the sales funnel where prospects are most likely to drop out.
They'll also be able to inform their colleagues in sales:
Which prospects are most likely to turn into leads
Which products or services they are most likely to buy
How much they're likely to spend
Content marketing is the creation and distribution of text, images, and videos for people at different parts of the sales funnel.
Distribution of marketing content is particularly important. That's because there's no certainty that a prospect will find your website when looking for more information. By placing your content on the websites and platforms they're most likely to visit, you have a better chance of being seen and getting them into your sales funnel.
One AI/ML function will examine the title of a piece of content and then recommend which of the many specialist, niche sub-publications on the very popular Medium website will deliver the greatest exposure to your target market.
Natural language processing is one of the functions many business people get most excited about.
A core business need is spotting problems as soon as possible so that they can be remedied. Text classification apps can instantly parse the contents of lead capture forms and customer support emails. They can then be programmed to take specific actions or be directed to certain teams or members of staff straight away.
AI and ML tools also use algorithms to detect positive and negative sentiments. They can be deployed to search social media sites for brand or product mentions, automatically retweeting positive content and forwarding negative content to relevant team members who can help resolve the issue.
Predict Customer LTV
Rather than focusing on upselling or reselling to existing customers, many businesses focus most of their attention on finding new customers. They're missing an opportunity.
AI and ML apps can examine a company's sales records and then segment customers using previous spending and demographic data to predict the estimated lifetime value of a customer. Specific marketing campaigns can be targeted at customers with higher LTVs to generate more revenue.
Other Impressive Applications
Different apps can improve fraud detection, help with employee retention, minimize predictive maintenance costs, and more. There are no-code apps already built for most business processes.
But many AI models now allow you to use your own data to come up with your own methods for business analysis and prediction-making.
For companies operating production lines, deep learning tools can immediately find defects within manufactured products during assembly.
For marketplaces and e-tailers, the same computer vision tools can be put to use to identify individual product and product categories for faster classification and better SEO.
Image regression involves teaching a machine what dimensions and characteristics it should expect to see from a particular type of product.
For example, by looking for deviations from the standard, it can help farmers identify if certain crops are failing to thrive. The insurance sector also finds image regression useful in determining the value of items claimed by policyholders.
AI and ML today can help analyze audio, video, image, and text data for suspected fraud. Or, they can review dynamic pricing so that online retailers can better compete with the likes of Amazon.
Tabular data regression allows businesses to make predictions about future outcomes based upon calculations arrived at by analyzing input variables.
Tabular data classification finds patterns within data. It's used by some of the apps mentioned earlier in this article that evaluate the strength of leads, lifetime values, and so on.
Although more complicated than off-the-shelf apps, tabular data apps allow you to use the data you have to create predictive models.
You can then connect the AI plugins containing the functionality you want to your other existing apps like Salesforce, Snowflake, and Google Sheets.
AI and the Digital Transformation of Your Business
Until recently, AI and ML tools have been reserved for companies with big enough budgets to afford coders, programmers, and data scientists. No-code AI gives smaller companies the opportunity to gain the same level of insight into what their customers want and how to improve business efficiency and productivity.
As experienced practitioners, we expect to see AI and ML form part of the innovation process in the coming years.
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