Startups vs. Monoliths: Who Does Big Data Better?

startupsvsmonolithsAs advanced analytics continues to become more sophisticated, businesses across industries are realizing how much business value there is in turning data points into actionable insights. With demand for big data ever increasing, a host of startups have emerged alongside the Microsofts and IBMs of the world to help organizations integrate, understand and leverage big data insights.

When it comes to big data, many people have the perception that bigger is better. They want to use services provided by the biggest companies, with the perception that the large the organization, the more access they must have to data. Big companies have so much first party data, they must know much more about consumers, right?

It may seem counterintuitive, but this is simply not true. With so many different data points out there, certain points will be more valuable to you in some instances than in others. In order to create a comprehensive data set, even the monoliths need to purchase additional points in order to fill the gaps in their own data.

Consider this in another way – a surgeon could have the choice between a scalpel and a machete. But just because the machete is bigger, doesn’t mean it is more useful to the surgeon. He needs the right tool that will allow him to make small, anatomical dissections. Having the right data is similarly important, allowing companies to target and personalize at a more precise level.

The real differences between startups and monoliths when it comes to big data has nothing to do with accessibility – and everything to do with service. Qualities such as adaptability, time to market for innovations, ability to be prescriptive and access to quality resources within the company will distinguish smaller companies’ big data services from the monoliths.

 

Adaptability

Startups have a big advantage over larger companies when it comes to adaptability. Shifting strategies with big companies is much more difficult for the simple fact that they have a lot more layers of management and much longer approval processes. Clients are often made to work within the monolith’s existing products and frameworks. Startups are leaner by nature, meaning their products are often more adaptable to your own business compared to those offered by larger companies.

 

Time to Market

In the same vein, innovations can go to market much more quickly with a startup. With high volume, high variety data that comes in at a high velocity through real-time data streams, it is important for companies to be able to rapidly experiment with all sorts of combinations of data, and adjust the tools they’re using quickly to successfully predict trends, identify customers and push out promotions at the optimal time. Without the lengthy cycles of a large corporation, startups are much more flexible and capable of working with customers to get them what they need, when they need it. Meaning if your data conflicts with how you’re currently running a campaign, you can change tactics right then and there. If a new technology hits the market, or you need an integration that doesn’t already exist within the platform, a smaller company is nimbler, and likely able to turn it around for you quickly.

 

Prescriptive Ability

In addition to longer decision and go-to-market times, large companies can become entrenched in the traditional way of doing things, which affects their ability to administer prescriptive and innovative advice. With more invested in the status quo, they run the risk of becoming reluctant to invest in new technologies and methodologies that might require an overhaul of their current businesses. Startups are not hindered by deep roots within a particular product, meaning they’re willing to try innovative solutions – focused on figuring out what will deliver the best outcomes, rather than protecting their own heritage.

 

Access to Quality Resources

Who you decide to do business with determines what – and who – you get access to. Larger businesses often have procedures that prevents their executive management from engaging in close contact with their customers, delegating that responsibility to scores of account representatives instead. While there’s no harm in this, there’s no real benefit either. When working with a startup, customers have access to management (and expertise) much higher up in the food chain – sometimes all the way up to the CEO. Because of this, startups often develop much closer relationships with their customers and take a vested interest in making their data programs successful.

 

When it comes to big data, perceived “volume” of data doesn’t matter. What you DO with your data, and how a company works with you to accomplish it, is much more important than the size of the business you’re working with. The next time you consider leveraging big data, ask yourself: what data do I actually need, and how will I use it to benefit my business? That’s the key to mastering big data.

CB Insights: 45 Artificial Intelligence Startups Targeting Retail In One Infographic

AI isn’t all self-driving cars and chess-playing computers. There’s an emerging market for AI use in e-commerce.

Investors poured a record high $1.05B into artificial intelligence startups in Q2’16, and AI is already affecting more areas of our lives than many people realize. Even retail and e-commerce companies are increasingly integrating the technology.

Recently there’s been a rush of AI announcements and acquisitions by major retailers: Just this week, Etsy acquired Blackbird to enhance its search functionality through AI, followed the very next day by Amazon acquiring Angel.ai (formerly GoButler), another AI-powered searching tool. And earlier this month, e-commerce unicorn Houzz (see our full unicorn tracker here) announced a deep learning initiative to help users find and buy products by clicking on images.

Using CB Insights data, we dove into the wide array of AI startups focused on retailers and e-commerce businesses, including AI-powered personal shopping apps, natural language processing and image recognition tools for shopping websites, predictive inventory allocation tools, and more.

The area is emerging, and most companies focused on retail AI remain in the very early stages. However, we have seen several larger deals in recent months. ViSenze, which lets users search e-commerce sites by image or find visually similar items, raised a $10.5M Series B in September, while Trax Image Recognition, which visually tracks the performance of goods on grocery shelves, raised a $40M Series C in June. Several startups are backed by top investors from our smart money list, such as search optimization tool Zettata, backed by Accel Partners, and predictive customer targeting platform AgilOne, backed by Sequoia Capital.

While there are numerous other AI startups focused broadly on personalized marketing and ad targeting, we limited this market map to startups whose core focus is retail and e-commerce. The startups in this graphic have raised roughly $650M in total disclosed equity financing. Scroll down to view the graphic, category explanations, and a full company list with select investors.

 

Retail AI market map

See the market map below. This market map is not meant to be exhaustive of startups in the space. Graphic includes private, independent companies only.

final-retail-ai-market-map

Category breakdown

We divided our market map into the 12 categories listed below:

Real-time product targeting – Machine learning to present online shoppers with personalized product recommendations. These companies typically update e-commerce websites in real time to present product selections best suited to the individual shopper.

Real-time pricing & incentives – Machine learning to adjust pricing, sale options, rewards, and coupons in real time to try to push hesitant shoppers toward conversion.

Natural language search – Algorithms that use natural language processing to improve search functionality in e-commerce websites.

Visual search – Image recognition platforms that help e-commerce websites let visitors search by image, instead of text, and match relevant products to specific images.

In-store visual monitoring – AI-powered software that analyzes photo and visual content of store shelves to help brands track how their products are stocked and promoted in real time.

Conversational commerce – Chat software and chatbots focused on helping shoppers make purchases in a conversational text format using natural language processing.

Predictive merchandising – Big data analysis to optimize purchasing, allocation, and product assortment across stores and e-commerce. The aim is to better predict demand in different geographies to avoid waste and prevent inventory from going out of stock.

Sizing & styling – AI-powered software to help retailers integrate improved product sizing and outfit-building tools into their websites.

Multichannel marketing – Startups using AI to create targeted marketing campaigns across desktop, mobile, email, and other digital channels. Inclusion limited to startups focused specifically on e-commerce.

Integrated online & in-store analytics – Startups that combine both digital and physical store analytics to help retailers better understand their customers.

Location-based marketing & analytics – Startups that combine digital and physical store analytics, while also integrating beacon technology to track shoppers’ locations.

 

 

Originally appearing on CB Insights “Don’t You Look Smart: 45 Artificial Intelligence Startups Targeting Retail In One Infographic”: https://www.cbinsights.com/blog/ai-retail-smart-shop-startups/