Artificial Intelligence (AI) is the new hype of today's digital world. Except, it really isn't - for this key reason: it adds value. That is what essentially business and capitalism are about - taking inputs, adding value and selling the value added goods or services for a higher price and making a profit in the difference between the cost and price.
Beyond the headlines and buzzwords, though, are some developments that will upend the way we think of work, and business - from getting quotes for a home roofing project to how work itself gets done. The key question is, at what cost? should businesses rush into AI, or step into it incrementally?
Depending on the size of the business, the industry and competitors, it may be one or the other. For smaller businesses, an incremental process may be a good way to gauge the promise, pain and profits of the new ways.
Smaller businesses do not have the capital to spend large funds. But the good news is that off the shelf commercial software, combined with vast data processing ability at low scalable costs (through cloud) are leveling the field. Likewise, talent is getting more widespread - regardless of what one might hear about an AI talent shortage at the high end (that's Google and Uber fighting over self driving cars and such).
Here is an example of data analysis and prediction of new roofing prospects in a medium sized suburb (50,000+homes) for a roofing firm, whose primary business is replacing old roofs on homes and smaller commercial establishments.
Expertise to create an AI modeled marketing campaign is easy and getting easier by the month. Thousands of aspiring data scientists, including college students, free-lancers, mid-career employees who have some data analytics knowledge and are eager to apply their skills - many of whom may do it for a small fee or even for free (in exchange for your data and a plug on LinkedIn or a recommendation). Many of these practitioners will probably use their own hosting or infrastructure resources.
Here's the value proposition - as opposed to more traditional methods, this is a possible scenario of how an AI inspired campaign might look like.
- Get satellite imagery of the town from commercial satellite imagery or existing free database if available.
- Use database file to filter out new homes, government properties and commercial grade properties that need to be excluded (flat concrete roofs, for example).
- Use image recognition and CNN (Convoluted Neural Network) processing to determine which roofs are in deteriorated condition.
- Use calculation of roof size (from image database) to estimate effort - roofing materials, labor, time to completion).
- Use property records to get owner details.
- Use more detailed databases to exclude properties in receivership (foreclosed properties).
- Use township public records to exclude properties that are abandoned, with tax liens etc.
- Send digital marketing offers to targeted lists.
- Physical flyers to targeted lists with discounts and financing offers.
- Joint marketing prospects with local bank branches (for financing and home equity loans).
- Joint marketing prospects with local real estate firms.
- Project end date (to close out campaign, tally costs and new clients) and season related end of activity (for example, in Northern areas, middle of Fall).
A post-completion analysis would, of course, ask these questions:
- How many new clients resulted from the campaign?
- Was it more or less than similar sized "older" campaigns (flyers, commercials) from the past?
- Once you factor out seasonal factors, was it worth it?
- Cost-benefit analysis
