Tracking the Shopper Experience. Interview with Craig Teich of Connexity

Craig Teich, CEO of Connexity, has a deep background in digital marketing, sales, ad technology, and business development, concentrating especially applying the latest technology to help marketers find new ways to reach and engage with the right consumers from gaming to retail. Connexity was previously known as Shopzilla and has recently launched a “Data as a Service” initiative.
According to Teich, “Connexity helps marketers find, get, and keep customers. We leverage proprietary online shopping data, deep consumer insights, and our own technology platforms to help marketers, brands, and retailers more effectively engage and convert consumers throughout the online path to purchase.”  

In this interview, Teich talks about his role in the company, the type of data they use, how they gain insights from the data and how he expects the media landscape will look three to five years from now.

CW: What do you do at Connexity?

CT:  I oversee the company’s media, marketplace, and data solutions activity on a global basis. This includes responsibility for product strategy, revenue generation and managing our client partnerships across retailers, brands, and agencies.   Collectively, my team manages over 8,000 customer relationships spanning the US, UK, DE, CA, and IT markets.

CW: What type of shopping data do you have?

CT:  We know a lot about what online consumer are shopping for, including the categories they shop in, the brands they shop for, and the stores they shop at.  We also have a strong understanding of the type of online shopper a person is, such as a luxury shopper, a deal seeker, or an online buyer.  As consumers engage with product listings in our e-commerce ecosystem, whether on our owned and operated sites or via our CPC marketplace platform, we capture these high intent shopping data points from this deep place in the path to purchase.

CW: How are you able to ascertain whether a shopper is a luxury shopper, a deal seeker, or an online buyer beyond your ecosystem. Do you purchase other company data? Do you capture behavior beyond your own ecosystem and if so how? 

CT: The depth and diversity of data we have in our own ecosystem makes it unnecessary to rely on 3rd party data. In the majority of instances we are able to understand consumers and their online shopping preferences through our data alone. For instance, if we find that a consumer is shopping for multiple luxury-related categories, brands and products then we would identify them in our audience management platform as a luxury shopper. The only time where we may need to integrate outside data is when we are establishing a new category that requires category-specific data for our audience models. For instance, we recently announced a partnership with IRi to expand our CPG targeting capabilities. IRi’s offline, CPG purchase data can now be used as a seed set for Connexity’s audience models. We’ll score our data against their seed set to find other similar CPG audiences. 

CW: Do you collect data into consumer segments that are proprietary?

CT: Yes. All of our audiences are proprietary to Connexity. 

CW: Do you know when a purchase is completed and that certain ads need no longer be pushed to that consumer?

CT: We always work with our clients to improve the performance of their campaigns and meet their specific goals. For instance, we’ll change the messaging based on where they are in the purchase cycle, so we can map the ads to prospecting, retargeting, or retention.  Likewise we’ll optimize how we target ads to focus on the best performing audiences and placements.  

CW: Does it cover all consumer product and services categories?

CT:  Connexity data covers the classic retail taxonomy of parent and child categories (Home & Garden, Clothing, Sports & Outdoors, Computers & Electronics, Appliances, Toys & Games, etc.) and also includes verticals popular in CPG such as Health & Beauty, Baby Care, and Pet Supplies.  The core of our audiences is driven by e-commerce data, and we create many types of segments with it.  Our thesis is that “shopping tells all”.  What a person is shopping for dictates not just in-market activity, but also their lifestyle, life stage, and seasonal interests.   We do not have data from service industries like Travel, Education, and Restaurants. 
But our lifestyle and life stage audiences can apply to these types of industries. For example, Education advertisers may be interested in reaching Millenials. Family restaurant chains may want to reach Parents. Travel advertisers may want to target adventure seekers.  Millennials, Parents, Adventure Seekers are all examples of Lifestyle or Life Stage audiences that we can identify through shopping behavior. 

CW: How do you connect shopping data to media usage?

CT:  Connexity has its own DSP, and so all of the shopping data we land in our audience management platform we connect to media via our RTB technology.  We connect to all of the major ad exchanges, and can directly activate our data across the programmatic ecosystem.  When advertisers run media through our platform, we call that “MaaS”, or Media as a Service.  As I mentioned above, we have just launched a Data as a Service product, “DaaS”, that allows us to move the same audiences we can activate in our platform into an advertiser’s platform of choice.  We team with Oracle BlueKai and LiveRamp to move data from our platform to a partner platform.

CW: What audience data do you use?

CT:  We use our own proprietary shopping data to create a library of audience segments.  We see demand from marketers and advertisers across all of the different types of audiences we run.  In-Market, Lifestyle, and Life Stage segments are the most popular for retail marketers.  The CPG and Auto verticals are more active with our lifestyle and life stage segments.  The financial vertical has a lot of interest in the heavy online buyers and those shopping for higher ticket items, as well as life stage audiences.  As I mentioned earlier, the core e-commerce data we sit on has many applications, making it versatile across multiple industries.

CW: Please give me an example of the data and how it is used.

CT: Imagine a consumer that engages in our retail marketplace and shops for Black and Decker power tools.  From this single data point, we classify this individual as in-market for Power Tools.  Big DIY and Home Improvement retailers love access to this fresh data to bring in a new buyer to their site or store.  Now imagine we see this same consumer shopping for flooring supplies, faucets, and DeWalt power saws over the course of two months.  While these data points create more in-market targeting opportunities, we also classify this shopper into a DIY lifestyle segment based upon repeat activity in a similar vertical.  The same retailers are interested in this shopper, but this persona is also interesting for banks (home equity loans), truck brands (DIY and trucks go hand in hand), etc.  The more we know about a shopper, the more we place them into in-market, life stage, lifestyle, or shopper type categories.

CW: Who is your competition and what is your point of difference.

CT: We compete in the marketplace with companies who use or supply data to target audiences through programmatic media. Examples might be Rocket Fuel or Conversant (media buying platforms) or eXelate and Datalogix (data providers). Unlike our competition, we are specialists in activating online retail audiences. We have access to vast amounts of first party and proprietary shopping data that is unique to the overall data marketplace. After Google, we drive the 2nd highest volume of quality leads to retailers, and these high intent leads generate data that is not matched by the competition. Our data is also unique in its diversity, as it is based on our relationships with over 8,000 different retailers. Finally, unlike other online retail data sources, we offer the flexibility to build and activate audiences either in our own platform or through the DSP of the advertiser’s choice.  Our volume, quality, uniqueness, and flexibility set us apart.

CW: How is your product groundbreaking?

CT: Connexity’s suite of audiences is groundbreaking because we bring a quartet that is not matched in the data marketplace for online retail data:  volume, quality (high intent structured data), uniqueness (our audiences don’t overlap much at all with competitors), and flexibility (you can activate in our platform or the platform of your choice).  Shopping in many ways tells all, and we deliver a view of the online shopper to the data driven marketing ecosystem that fills a gap today, and that advertisers and partners are very pleased to see.   

CW: What insights can you get from your data that you can’t get from other data sets?

CT: With our understanding of categories consumers shop in, stores they shop at, and brands they shop for, we have a unique set of online shopping insights that we can offer.  We have clients that provide us first party data, and we are able to help them better understand key shopping behaviors that aren’t readily available from the 3rd party data marketplace.  Because shopping also dictates certain lifestyle and life stages, we are able to help marketers understand certain personas that match strongly to their brand and offering. 

CW: Give me some predictions on how you expect to see the media landscape in the next 3-5 years.

CT: If I had to take out my crystal ball, I would focus on two themes that will shape the journey ahead for the media space in the next 3 to 5 years; consolidation and specialization.  Platforms and technology will move in these directions, though data will continue to play across the ecosystem.
On one end, I see companies like Oracle, Adobe, IBM and others looking to create the full suite of services for data driven marketing within an integrated MarTech stack.  We’re already seeing this type of consolidation, but I expect the pace to pick-up as new services are added to the mix.   On the other side, I  see a place for specialists to still thrive and feed what should remain a relatively open market.  Connectors like LiveRamp will play a key role in this open community.   I don’t expect the dominant players like Google or Facebook to bring down their walls anytime soon, but I do see most publishers keeping their doors open for business making room for companies that can help create incremental value; whether it’s creative, measurement, attribution, or new formats. Companies that focus on being best in breed will survive and in some cases get added to the larger tech stacks.

So, fewer platforms, more data, but marketers will still have options; they can choose to work under one roof or spread their bets amongst specialists.  In terms of where Connexity plays in this prediction, we see our technology standing out as a best of breed player with retail expertise, while our premium shopping data will help fuel marketing across both the open and closed gardens.

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