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Using Artificial Intelligence To Predict Purchase Intent

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Using Artificial Intelligence To Predict Purchase Intent


The use of AI in marketing allows businesses to understand customer demand for their products and their willingness to purchase them.

Predicting the purchase intent of anonymous visitors from various sources can be a daunting task for your business. Earlier, businesses would gauge customer interest in their products based on factors such as past demand, prices, and rival performance. Today, those factors are still relevant, in addition to a few more such as behavioral trends, reciprocity and product scarcity in the market. Such factors create huge reserves of customer data that need to be scanned and analyzed to get an idea about purchase intent. AI is the perfect tool to find patterns from big data before using them to create marketing and product promotional strategies. As we know, the combination of AI in marketing already has several applications today. Tools such as AI, machine learning, computer vision, and Natural Language Processing (NLP) allow you to go through thousands of data points online to understand each individual’s behavior. Unlike humans, AI can evaluate thousands of data points before carrying out a comparative analysis between the number of possible permutations and the billions of known outcomes. AI algorithms are trained with millions of datasets to predict buying intent in real-time based on such factors. Here are some of the ways in which AI goes about the purchase intent prediction process:

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Identifying Customer Pain Points

Pain points are specific problems your prospective customers are facing with regards to your products in the marketplace. Detecting these points allows your business to know the specific aspects of your product, marketing strategies, or other factors that you will have to modify as per customer requirements. Identification of pain points before addressing them allows you to understand how to purchase intent works for specific products and how you can increase it. Pain points are assessed by conducting qualitative market research and feedback analysis, which can be optimized with the help of AI.

Data can be collected from customers in the form of the voice of customer analysis, surveys, purchase ratings, social media, and others. Customer feedback analysis tools enable you to integrate them with your CRM systems. Once this arrangement funnels in information related to customer interactions, customer satisfaction index, data generated from chatbot interactions with customers, and other similar details, you can use AI to get closer to predicting your customers’ purchase intent by analyzing this data. The AI-enabled prediction of purchase intent using pain points is useful in scenarios where you have information available in the form of market research data and customer feedback. However, how can you predict purchase intent for anonymous visitors on your site?

Predicting Intent from Interest Signals

When direct information is not available, your AI algorithms can collect data from other sources. Generally, visual content consumption is a powerful indicator of purchase intent. There are several third-party service providers who collect user behavioral activity data to understand the factors that drive purchase intent. From such data, AI can forecast the purchase intent for your products by using a few signals, which include:

  1. Number of consumers who visited your website
  2. Time spent on your online page
  3. Scroll Speed
  4. Types of products viewed and
  5. Time spent seeing each product

Predicting the purchase intent of your customers and others is challenging and full of guesswork. Using AI in marketing for the task reduces the guesswork and makes the process more data-driven and the predictions accurate.



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TECHNOLOGY

SaaS pricing inflation growing 4x faster than market inflation

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Cloud Computing News


Inflation has dominated the financial news landscape in 2022. In many markets, the consumer price index (CPI), has reached its highest point in a generation. This growth in the cost of ‘things’ also applies to software.

Almost every organisation has come to rely on SaaS to conduct business, from communications tools like Slack and Zoom to productivity suites like Microsoft 365 and Google Workspace, as well as department-specific platforms like Atlassian, Workday, NetSuite or Salesforce.

This is according to a report into SaaS inflation pricing from Vertice, a SaaS purchasing and spend management platform.

Spending on SaaS products grew more than tenfold between 2010 and 2020, from $13b to $157b annually. Investment accelerated even faster at the onset of the coronavirus pandemic, as companies raced to support remote working. SaaS spending increased by 26% in the months following the initial lockdown in 2020 and has only continued to grow in the years since.

Unlike many other significant overheads, like payroll and rent, the selection, management and renewal of SaaS are decentralised in nearly every organisation. This is for a variety of reasons, but buying power plays the most important role. Buying power typically sits across several individuals and departments, with finance leaders managing budget requirements, IT teams assessing systems and compliance considerations, and department heads selecting based on functionality. It’s a complex web of decision making and, even with the best intentions, it can be a struggle to gain a single view of all of the SaaS products a company uses.

This ‘wild west’ of a cost centre is a significant problem when the share of the total cost is considered. A growing percentage of all expenditures for businesses goes to SaaS, with around 12.7% of total spending now used on software investments. That means $1 in every $8 that modern organisations spend is now dedicated to SaaS. To translate that into dollars — as of 2022, companies spend around $3,112 per employee each year on SaaS. This figure rises to $4,552 for technology companies, who spend more than firms in any other category.

It has taken only five years for average SaaS spending to double. Based on the economic inflation rate over the same period, it would take 18 years for the cost of SaaS to double. This growth has far outpaced the rate of general economic inflation, even after factoring in recent periods of an uncharacteristically high CPI.

Clearly, the impact of SaaS in terms of productivity, collaboration and inclusion has been significant – but the accompanying cost has also been quietly spiralling upwards.

Analysis of more than 10,000 SaaS contracts shows that 74% of vendors have increased their list pricing since 2019. Among the quarter of vendors that have not, almost all have reduced the size of the average discount afforded to customers – effectively raising the spend without touching the list price.

A comparison of regional inflation rates with the SaaS inflation rate by geography reveals that over the past five years the cost of SaaS for US organisations has grown 3.5x faster than the general inflation rate – even after accounting for an exceptionally high national inflation rate in 2022.

SaaS inflation has outstripped general inflation rates even more rapidly elsewhere; spending at British and Australian firms has risen at a rate five times greater than regional economic inflation.

Joel Windels, VP of marketing at Vertice, said: “It’s become clear that not only is SaaS critical to modern businesses, but also that it represents a growing cost centre that can rapidly spiral out of control without strategic management. Even without investing in new tools or added licences, the data shows that spending on SaaS is exploding. With an uncertain economic outlook for 2023, finance leaders absolutely have to start taking a more considered approach to SaaS spending if they are to maintain growth and streamline their operations” 

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