DDBA 8161 Week 9 Build or Buy Memo Example

Memorandum

To: Chief Claims Officer, Prairie Mutual Property Insurance

From: Strategy analyst, DBA student consultant

Date: October 10, 2026

Re: How to develop claims analytics capability

Recommendation

Build a claims-analytics team inside the claims function over two years, licensing standard modeling tools rather than acquiring an analytics vendor. Buying would be faster: an acquisition would deliver working models for fraud screening and repair-cost estimation within months, where building will take eighteen to twenty-four months to match them. The case for building is that anything we can buy on an open market, a national carrier with deeper pockets can buy too, and the part of claims analytics that would be hard to copy runs through our own adjusters and our own claims history.

The Case for Buying, at Full Strength

Prairie Mutual, a composite regional insurer writing homeowners and farm property policies in four Plains states, pays about $310 million in claims a year. Two analytics vendors are for sale. The stronger one, a 40-person firm, has fraud-screening and repair-estimation models already used by eleven insurers. Buying it for a reported $28 million would put working models in our claims process this year, along with experienced data scientists who are hard to recruit in our region. If models save even 1 percent of claims paid, the savings would be about $3 million a year.

Why Bought Capability Will Not Set Us Apart

Barney (1986) argued that a resource bought in a competitive market is priced close to the value buyers expect from it, so buying it earns an advantage only through luck or through information the seller lacks. Analytics vendors are sold in a visible market, to bidders including national carriers that write in our states. If the vendor's models are worth $3 million a year to us, they are worth at least as much to a larger rival, and the price reflects that. And the vendor's eleven existing clients already use the same models; buying the firm buys what they have.

What Would Be Hard to Copy

Two things we hold are not for sale. The first is our claims history: about 180,000 settled claims over fifteen years on rural and farm properties, with adjusters' notes on hail, wind and equipment damage specific to our territory. A national carrier has more claims in total but far fewer of this kind. The second is our adjusters' judgment. Our best adjusters can tell from photographs and a phone call which hail claims will need a roof replacement and which a repair. That judgment has never been written down.

Dierickx and Cool (1989) argued that assets built up over time resist imitation because the time cannot be compressed. Models trained on our history and shaped by our adjusters' knowledge would be such an asset. A rival could buy the same tools but not our claims or our people's experience with them.

The Risk of Building

Capron and Mitchell (2009) found that firms fare better when they match the route to the size of the capability gap, building inside when the gap is modest and acquiring when it is wide, and that internal frictions often push firms toward the wrong route. Our gap is real: we have no data scientists today, which by that logic favors buying. That is why the recommendation licenses standard tools and hires a small team of four, led by one experienced analytics manager, and pairs each data scientist with two senior adjusters. The aim is not to invent modeling methods but to apply standard methods to data and judgment no one else has.

Cost and Timeline

Building would cost about $1.6 million a year for the team and tools, or about $3.2 million over two years, against $28 million to acquire. The first models, for repair-cost estimation on hail claims, should be in use within twelve months.

When Buying Would Be Right

If a severe hail season or a regulatory change made fast fraud screening urgent, the speed of acquisition would outweigh its lack of distinctiveness. Absent that, build.

References

Barney, J. B. (1986). Strategic factor markets: Expectations, luck, and business strategy. Management Science, 32(10), 1231-1241. https://doi.org/10.1287/mnsc.32.10.1231

Capron, L., & Mitchell, W. (2009). Selection capability: How capability gaps and internal social frictions affect internal and external strategic renewal. Organization Science, 20(2), 294-312. https://doi.org/10.1287/orsc.1070.0328

Dierickx, I., & Cool, K. (1989). Asset stock accumulation and sustainability of competitive advantage. Management Science, 35(12), 1504-1511. https://doi.org/10.1287/mnsc.35.12.1504