Q&A with Ann Caven: Vice President of Sales

You’ve been in sales for quite some time. What’s kept you committed to the field and what do you enjoy most about it?

I’ve been in sales for about 20 years. For me most sales are relationship based. People like to conduct business with people that do what they say they are going to do, are honest, know their product and are creative. I like being that person.

Right now, the aspect of my role that I enjoy the most is strategizing on anything: prospective clients, sales messaging, marketing materials, how to close a sale, how to get the client to the next level of a “close.” My role allows me to take a project that is very specific and intricate and make it something accessible and interesting for a potential client.

What drew you to PREDICTif originally? What has kept you drawn here?

Discovery drew me to PREDICTif. I’m a salesperson, but I can’t sell just anything. I need to be interested in what I sell in order to connect with clients and to solve their problems. PREDICTif had the added benefit of targeting clients with business I often know nothing about. I like that part of the PREDICTif work too: getting to understand a client’s business. It adds a layer of complexity. Discovering how our abilities meet their needs, what’s important to them, how do they know they are doing a good job, what are their points of evaluation? It is all very interesting to learn about a new product, figure out that partner environment AND get to discover the details about major clients.

Could you tell us about your life outside of work?
I have two kids, Amelia and Grey, who inspire me daily. Amelia is a student at the University of Texas and Grey is in high school. They work hard. My favorite hobby is textile art (meaning I sew). I make quilts and decorative objects. For the last 5 years I have shown and sold my work in an art sale on Montrose. My mom taught me to sew. She was a civil engineer and when she stopped working to have kids she started sewing and taught me.

I also really like to cook. We don’t eat out a lot. We take turns cooking and I like to help even if it is someone else’s turn. Lasagne, Lomo Saltado, Steak & Ale Stew, Chicken-in-a-Pot, Chilaquiles, Stromboli. I try new stuff all the time, but these are a few we revisit often. To clear my head, I take my giant Doberman for a walk. He looks menacing but is a marshmallow. Nobody comes near us. I just walk.

What are three things most people don’t know about you?

• I speak Russian. I don’t speak it well anymore – I was never fluent – but I was confident enough to travel there alone and get along on my own linguistic abilities. I wanted to study something with entirely different characters, but Arabic wasn’t offered that term. The only other language offered that didn’t use Latin characters was Russian, so I went with it. Russian language/literature ended up becoming my minor in college.
• I love watching Smithsonian National Zoo’s panda cam. I might even watch it more than I should… The panda that was born about 5 months ago, Xiao Qi Ji, started out as a little ugly worm and in 5 months has turned into a miniature panda that can walk and play. I enjoy watching the mother, too. Mei Xiang has to be the most patient mother EVER.
• The ability I wish I had would be juggling, making balloon animals, or really anything to do with the circus. No particular reason. I saw another mom at a kid’s birthday party, who I never would’ve guessed could make balloon animals. There she was in a dress and pearls making pink Dachshund balloon animals for everyone. She WAS the party. Everybody needs party skills!

What’s the most satisfying project you’ve worked on at PREDICTif?

It’d have to be a win/loss analysis for a manufacturing leader. We started a conversation with a major Houston manufacturer who produces heavy equipment. Initially, they were interested in managing their inventory more effectively. They didn’t have great data for that but did for the Win/Loss Model. 

In the Win/Loss Model we use the client’s existing data to evaluate several dozen variables, analyze their affect and show how they work together powerfully to impact sales. It frees up the client’s time from playing with the price to help drive closes and forecast more accurately.

They sell their equipment through dealers. Our client has access to open bids – bids not yet won or lost and have hundreds of open bids from across the nation at a time. They wanted to set the price in a more calculated way using less “tribal knowledge”. That said, they had suspicions, too. They wondered if it made a difference to the win/loss of a bid if it ages? Do warranties help the win? Do dealer practices like inventory contribute to losses at certain times of the year? 

We took several years of data on both won and lost bids to make a predictive model and were able to show them the impact of price, which is huge, along with several dozen other factors they could also impact. We were then able to show our client how multiple factors work together to impact a won or lost bid. Two positive factors together, in this case, are more powerful than the same two positive factors viewed individually.

Can you tell us a little about working at PREDICTif?

One of the most interesting things about PREDICTif is how it fits into the Houston community. Houston is international: from the mix of cultures to ethnicities to industries. PREDICTif reflects that diversity in both our people and the industries we cater to. Very few people in the office are from Houston; most are from different states or countries. We love it. We also serve a variety of growing industries from healthcare and digital tech to manufacturing and trade.

The most challenging aspect of working here is keeping up. The data scientists are incredibly brilliant people and I feel like there is little I can contribute to past the sales effort. That’s also part of what I like most about PREDICTif: the people. They are encouraging, they work hard and they take chances. They’re funny, too. Our CEO, Jeff Huang, advised me at one point that I didn’t exhibit enough “swagger” in sales calls and meetings. So now we have a “confidence meter” on my whiteboard with “Ann” at one end and “Arrogant” at the other. Jeff occasionally pops in and circles different tick marks in jest. It’s been very beneficial in helping me realize that I worry too much about conflict when making suggestions, but even if a suggestion causes discord, overcoming it as a team is a critical part of accomplishing and growing.

If you could go back in time and give yourself some advice at the beginning of your career, what would you say?

Keep asking questions. People may come across as annoyed initially when you ask a ton of questions. Most of the time they realize the questions were leading you to a great idea or they come to understand “if Ann was confused”, we might not reach the other “Anns” in our audience. Also, don’t put too much information in email – nobody reads it.

What’s a challenging but important thing to do as a leader?

Not to simply manage. There are leaders that are just managers. That’s alright, but it doesn’t take a business to the next level. The “manager as leader” environment tends to be very political too, which is a distraction. I don’t enjoy that. I don’t “politic” well. I prefer to work for leaders that think of new approaches, that pivot, reevaluate, and listen then plunge in. I strive to be that kind of leader.

What advice do you have for prospective PREDICTif candidates?

This is the place you want to be if you are interested in learning about businesses of all types, finding the “suspicion” clients have about their business/data, and/or discovering the right service for those clients to help them justify or disprove the suspicion with data. We’ve talked to some very large companies that have embraced the Cloud and have data lakes with tons of information flowing into them. We have enormous success when a person at one of these companies decides to stop just collecting data but makes that data work for them; makes that data answer questions that come up regularly. We don’t necessarily need exactly the question the company wants answered, but we need a general pain point or an idea that centers on that historic data; a suspicion. Maybe the company wants to know generally: “When should we shut down for inventory?” We can bring back data that provides the answer and then makes them ask “why do sales fall off in early Fall?” “Why do we slow down at this time regularly?” and then “How could we make more money in October?” Now we have an ROI for them. We can use data to provide a few possible answers and answers to questions the company didn’t even know they had.