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Interview with Jon Armstrong, Adlucent

As an online retailer, what's the best way to find and attract online consumers to buy your products? And, how do you do it profitably? Austin's Adlucent (www.adlucent.com), headed by Jon Armstrong talked to us about how the company is applying big data analytics and software to the online, paid search market, to help those retailers better find and land customers.

What is Adlucent?

Jon Armstrong: Adlucent is a big data analytics and technology company that specializes in e-commerce. We work with retail companies, and monetize our data and technology through paid search. We help our customers grow profitable revenue, and improve overall business performance, through the real-time consume demand channel of paid search.

What kind of retail companies use your services?

Jon Armstrong: We represent over 130 different brands, and work with customer like Free People, Buy.com, and Oriental Trading Company. They are companies that sell all kinds of products online, from clothing, to consumer electronics, to party supplies, to office supplies, you name it. We focus on retail because our technology is specifically honed for retail customers, and helps them and us to deal with a bunch of dynamic factors that are at play with online marketing and the e-commerce marketplace, factors such as seasonality, inventory fluctuations, pricing, and promotion changes, which happen daily. Our analytics platform allows our people to achieve a competitive advantage for our customers, reacting to market dynamics faster and more precisely than our competitors.

How did the company start?

Jon Armstrong: It's a very interesting story. Our founders started working together when they were going to school here at the University of Texas. Michael was getting his Ph.D. at UT, and Nick was an undergrad at UT. They started working with Amazon in 2001, more as a hobby and a way to make money during college. They started having success, and by 2005, they had achieved quite a bit of scale working with Amazon on a performance basis, which paid them when they drove revenue. At the time, Michael scanned the industry looking for technology to solve the problems on Amazon, and didn't find anybody that was dealing with all of these market dynamics, which were changing very rapidly.

So, he embarked on building a technology platform that would do the campaign management, and handle the bid management efficiently, and integrate with search engines. More importantly, he built the retail specific analytics capability, that allows us to look at the data in lots of very interesting ways. We were able to get smart about consumer demand data and take action before our competitors. We officially started in 2005 and early 2006, and since then have continued to grow aggressively and build an interesting company.

Is there a lot of data and software behind all of this?

Jon Armstrong: Yes. What we're trying to do, is provide a lead that results in revenue for our customers. There lots of variable on what the value of a lead is, and whether or not they will convert. We've built technology that allows us to capture the various type sof data, from consumer intent, to product data, to product attributes, to commerce data, and merchandising data Our technology pulls all of that up on a daily basis, so that we're able to look at it in terms of a search engine might look at it, by keyword, brand, styles, categories, subcategories, and promotions. We're able to attract people to a promotion, by using that technology, which allows us to adapt fluidly to those conditions.

What makes customers want to work with your company, rather than trying to figure it out themself?

Jon Armstrong: I think customers realize there is a deep science to what we do. They're constantly making that determination on whether they should do this in-house or use a specialist. What we do is a very quantitative business, but quantitative analytics are not a typical marketing skill in a marketing department. More and more in the digital world, those are, however, super important, and because things are so much more measureable now at a granular level, we've seen the trend of customers continuing to work with partners in what we do. Some of the larger customers might work with general agencies over a whole host of agencies, which is a different way to solve the problem, but that addresses breadth, not depth. We provide depth of expertise for the largest channel in online marketing. Paid search is right at around fifty percent of online marketing spend, in aggregate, and is by far the largest channel. It takes specialized skills to analyze all of that data on a daily basis.

What's the biggest challenge facing your customers and the industry?

Jon Armstrong: As the business get more and more sophisticated, the tools that a buyer is using and influenced by gets more and more broad. I think the industry is going to continue to struggle with understanding the attribution of how those different channel influence someone in the buying process.

As an example, today, there is a big buzz about social and mobile, but the ROI for advertising spent on those channels, is nowhere near something measureable like paid search or an email campaign. Today, the large discrepancy between the time we, as a consumer, spent on things like mobile and social. The advertising spend is very low, relative to the amount of time we spend there, so the industry will have to continue to appropriately attribute values of those different channels and how they influence people in their buying process It's a very difficult channel to understand if someone sees a mobile ad on their phone, but purchases a product on their iPad or at their work computer, and connect that all together. Lots of people are trying to solve that, including Google and others, but as a customer, you need to continue to get smarter about your allocation and mix of marketing spend across all of those channels. You need to understand if some might have more ROI than another, because a perfect view of somebody in their buying process is next to impossible.

Thanks!


 

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