- Global investment in AI startups has slowed in H1 2022, after reaching a record-setting $51.29 billion last year in the US alone.
- Today, brands can apply AI to transform customer interactions, resolve pain points faster, and communicate with customers in more diversified ways, among other possibilities.
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Advanced data analysis, powered by AI and machine learning (ML), now offers multiple use cases for decision makers in nearly every industry. This strategy—broadly involving collecting and examining data to inform better, more predictable business choices—is not only imperative to company growth, but to innovation, as well.
As more businesses begin to integrate AI-enabled data warehouses into their solutions and products, those tapping into startups’ technologies will be ahead of the game. Today, data analysis is becoming more complex—thanks to advancing technology—so businesses would be wise to invest in agile startups solving long-term problems in customer experience. Many of these startups are also uncovering new capabilities that larger companies take advantage of, making data analysis increasingly commonplace across all types of organizations, including retail, finance, and civic enterprises.
Here, we break down the different forms and functions of data analysis, to see why it may help drive innovation and value for your business.
Data collection: data science, big data, and data warehousing
When interpreted by AI and ML technologies, big data sets can help identify patterns and insights into client behavior. Whether it’s stored physically or in the cloud, data holds the power to hyper-personalize products and client interactions.
One area of recent growth in client tracking is the location data market. A demand for location data has created a diverse marketplace of location intelligence firms that interpret the data, data companies that append data or provide clean rooms for analysis, and Big Tech companies that collect the data to better advertise or provide more relevant search results.
However, government- and Big Tech-imposed privacy restrictions have made data collection more difficult. With the end of many third-party data collection practices, companies are storing or sharing more first-party data than ever. But this shift can easily lead to increased potential security risks, which is why privacy and security efforts should go hand-in-hand with data sharing and storage.
AI in data analysis
AI applications are becoming more common across functions including supply chain, product, and back office. Brands are using AI tools to draw data that generates deep customer insights, tracks supplier pricing, and more. And it’s making a difference: more than 80% of IT professionals in marketing and sales worldwide believed AI data insights aided in a reduction in costs, with more than a quarter (27%) reporting a decrease of 20% or more, per a June 2021 survey by McKinsey & Company.
Customer service remains one of the most popular applications for AI tools in non-AI companies. Conversational technologies like voice assistants and chatbots to manage customer interactions bring immediate value to companies and provide data to help streamline otherwise time-constraining processes.
In retail, AI applications can help brands reach strategic goals, such as providing the customer a seamless online and offline experience through omnichannel fulfillment and “smart” store digitalization.
AI can help in physical stores—by automating checkout and loss prevention measures, for instance. Startups especially are utilizing these new data points to power technology developments.
AI also has B2B data applications. AI tools can help B2B sales and marketing leaders more efficiently perform processes like prospecting and targeting. In fact, B2B marketers in the US are using AI to optimize ad and conversion funnels and retargeting programs, as well as to measure specific KPIs, according to a February 2021 survey by Ascend2.
AI in descriptive analytics (What happened?)
AI continuously learns from current and historical data sets to better understand what drives positive results and what doesn’t. Descriptive analytics answers the question, “What happened?,” unpacking the progression of certain business situations or outcomes. It also helps identify trends from which strategists can apply their own hypotheses to draw conclusions.
In fact, many marketers already implement this form of AI in this way without knowing it, such as using a Google Analytics dashboard, an automated report on Microsoft Excel, or even through some functions on a CRM.
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AI in diagnostic analytics (Why did it happen?)
Diagnostic analytics answers the question, “Why did it happen?,” discovering the drivers of a business situation or outcome. Companies that have identified customer pain points can implement diagnostic analytics to determine its cause, then develop a roadmap for improvement.
For example, grocery stores that recognize a dip in click-and-collect purchases may dissect website data to discover if their customers are shifting more toward direct home delivery.
AI in predictive analytics (What will happen?)
Predictive analytics answers the question, “What will happen?,” employing ML techniques and statistical modeling to identify the likelihood of future outcomes based on historical data.
Wealth managers, for instance, use predictive analytics to make accurate predictions—such as identifying which clients are most at risk of leaving—to then guide a strategy for retention. At the same time, Starbucks can use predictive analytics from loyalty cards and its app to suggest personalized discounts.
AI in prescriptive analytics (How to make it happen)
Lastly, prescriptive analytics answers the question, “How do you make it happen?,” leveraging data to shape a plan of action.
Marketing professionals, for example, can dig into email campaign data—for send-time optimization and personalization, segmentation, and targeting—to achieve the most engagement. Alternatively, data sourced from app usage can give developers the opportunity to invest in tech catered to specific user needs.
Top AI startups and companies for data analysis and business intelligence
Despite pullback in funding and exits during the first quarter of 2022, AI startups still have a fair chance—thanks to the accelerating convergence of technical breakthroughs, leadership buy-in, and deployment across different business functions. The pandemic has driven a lasting surge of government and investor interest in AI, as its ability to help the world go remote has been tested and proven.
Here, using key findings from Insider Intelligence’s Top Startups in AI 2022 report, we reveal some of the top companies developing significant AI applications in marketing and retail, pioneering platforms and usability innovations:
- Founded: 2012
- Stage: Series C
- Total funding: $66 million
- What it does: Delivers an AI-generated language platform that can create content, and decipher what will best resonate with specific customers, through its algorithm.
- Why this matters: Content-generation technology enables marketers to personalize content for specific customer segments, which can lead to higher engagement. The tool can help marketers target customers in a more meaningful way and drive better campaign results.
- Founded: 2018
- Stage: Series B
- Total funding: $45.2 million
- What it does: ML performance monitoring platform that uses AI to validate, explain, monitor, and analyze algorithms for biases or other distortions. Key applications include applying insights to reduce churn—by evaluating why customer interaction with a company stops and how to counteract—and facilitating a company’s AI governance for hiring and other functions through explainable algorithms.
- Why this matters: As ethical AI becomes more important to companies, Fiddler AI is enabling a wider range of players to monitor algorithms and avoid risks posed by AI bias and error.
- Founded: 2014
- Stage: Series C
- Total funding: $73.5 million
- What it does: Offers an AI-native end-to-end contact center as a service (CCaaS) solution; product enables companies to implement virtual assistants in a no-code context.
- Why this matters: More companies can integrate conversational AI technologies regardless of tech talent.
- Founded: 2020
- Stage: Seed
- Total funding: $4.5 million
- What it does: Enables sales professionals to automate the outbound sales research process through data automation.
- Why this matters: The tool saves companies’ business development representatives time on typical activities, like researching prospects, and more fully prepares teams for sales meetings using the AI research tool.
- Founded: 2015
- Stage: Seed
- Total funding: $11 million
- What it does: Subscription technology platform for brands to gain new customers through trusted creators, rather than through sponsored content or ads.
- Why this matters: Using AI can help brands find new customers and creators to forge new brand partnerships. The technology has the potential to change the traditional marketing model.