Overall Context

There is a two-fold purpose to the analyses contained herein:

First, we want to understand if parts of the market are more performant than others, even if we don’t know why.

Second, we want to understand if there are any particular factors which increase the likelihood of a visit, to help find other places where we should expect a visit. Basically, can we explain why certain parts of the market are more performant.

Data Set Description:

  1. Looking at visits from FY22 Q4 forward.

  2. The factors we looked at - Geographic (State), Company Structure (Public/Private), Industry, Revenue, Employees, DEI Contacts, Account Structure, and Marketing engagement.

Takeaways:

  1. Using a decision tree algorithm to guide our thinking about how to order the most important factors:

    1. Size of the company - Employees then Revenue (high correlated)
    2. DEI Titles at the company
    3. Industry
    4. State

    The way to think about this is like a decision tree. First, sort your market based on company size, then split it by whether there are DEI titles at the company, and so on.

  2. We have 5-6 times the number of private accounts in our database, yet we are sourcing visits at roughly equivalent volumes from Private and Public companies. We are not getting in the door at private companies at the rate we need to support our visit goals.

  3. We’ve been seeing a decreasing trend in the number of visits we are sourcing from public companies and an increasing trend of visits from Private companies - in part due to outreach penetration.

  4. The presence of a C-Level DEI contact vastly increases the probability of booking a visit: 18% absolute increase (~1000% relative increase). However, only about 5% of companies have a C-Level DEI contact.

  5. In the absence of a C-Level DEI contact, we can use the volume of DEI dedicated contacts as a proxy for DEI commitment. Even with only a single DEI employee, we start to see healthy lift (~8% absolute) above no DEI contacts. And as the number of DEI employees increases, so do the probability of visit.

  6. Some states have proven to be problematic even though they have a wide variety of industries: FL, GA, MI.

  7. Size of the company is particularly diagnostic of our ability to secure a visit. In creating optimized buckets, we see clearly that 11K accounts have low penetration - they are 144M to 1.4Bn and under 5K employees.

  8. Inversely, we see that the scale works in the other direction, with visit acquisition increasing nicely with revenue.

Next Steps

  1. Fixing Account data and making sure training is consistent.

  2. Re-Run CapDB to create scores

  3. DEI Website Inclusion

  4. Build a predictive algorithm

Inspect Our Factors

Before we get into anything more complex, we need to understand how our factors are distributed and how the visits are distributed within them.

Starting with the structure of the company.

Visits by Company Structure

  1. Visits are roughly even between public and private companies.

  2. HOWEVER, the Private market is substantially larger, so our penetration here has been significantly less.

Visits by Company Structure
Structure Accounts Visits Percent_Visited
For Profit 1 1 100.0%
Private 11575 253 2.2%
Public 2469 274 11.1%
Unknown 4 2 50.0%
NA 2975 219 7.4%

Distribution of Visits at Structure Across Time

Data Caveat: One thing to note is that we are looking at the first time an account received a visit in this graph. For example, FY24 Q1 has 135 visits, but the sum of the bars will be a little bit less due to revisits.

  1. We have 5-6 times the number of private accounts in our database, yet we are sourcing visits at roughly equivalent volumes from Private and Public companies. We have to figure out a way to increase our outreach efficiency with private companies, if we want long run success of this product. <<Opinion: Anecdotal evidence suggests that organizational hierarchies and purchasing process tend to differ across company structure. I also believe there might be another lurking variable which could also be driving this result (e.g. size of company driving the need to attract diverse workforce).

  2. Notice that until this previous quarter, we are seeing a positive trend in visits for Private companies, and a somewhat downward trend for Public companies.

  3. The serious increase in FY24 Q1 visits at accounts where we have not specified structure is an interesting development, as the visit volume was relatively consistent before then. Examples of these companies are:

AstraZeneca, Barclays, Baccarat, Duolingo, etc.

Outreach Distribution to Structure

I think an obvious question is whether we distribute our outreach equally across these structures. At least in proportion to the

  1. There is some evidence to suggest that the declining trend in Public company visits correlates with the decline in outreached public companies.

  2. The Private company increasing visit trend appears to correlate with the increasing rate of outreach to these companies.

  3. The implied conversion rate on the NA bucket is very high, and I’ll need to better understand this component of the data.

Firmographics by Industry

What do we need to know about Industries?

  1. On average, the median value (1bn) of revenue doesn’t vary too much across all of the industries. A few notable exceptions, Organizations appears a bit lower, Insurance appears a bit higher.

  2. You see some very large companies in Manufacturing, Government, Business Services, Finance, & Retail.

  3. There is a correlation between the structure of a company and the revenue: public companies have almost uniformly higher revenue than their private counterparts.

  4. Unsurprisingly, this flows through to the employee count of the company as well: public companies have more employees, on median.

  5. C Level DEI contacts are much more likely to exist at public companies than at private companies, although the sheer volume of Private companies leaves us with more C Level DEI contacts at Private firms.

Revenue by Industry

Revenue by Industry and Structure

Employees by Industry and Structure

C Level Contacts by Industry

C Level DEI by Industry
Industry Accounts Outreached_Accounts Account_Pen_Outreach Visits Outreach_to_Visit Pipeline_Units VtE Units Eval_to_Unit
Agriculture 121 24 19.8% 2 8.3% 1 50.0% 0 0.0%
Business Services 1903 367 19.3% 50 13.6% 36 72.0% 4 11.1%
Construction 628 162 25.8% 14 8.6% 8 57.1% 3 37.5%
Consumer Services 280 12 4.3% 2 16.7% 2 100.0% 0 0.0%
Education 37 8 21.6% 0 0.0% 0 NaN% 0 NaN%
Energy, Utilities & Waste 449 155 34.5% 20 12.9% 15 75.0% 4 26.7%
Finance 669 248 37.1% 35 14.1% 27 77.1% 4 14.8%
Government 489 145 29.7% 13 9.0% 7 53.8% 1 14.3%
Healthcare Services 233 44 18.9% 6 13.6% 3 50.0% 0 0.0%
Holding Companies & Conglomerates 36 10 27.8% 6 60.0% 4 66.7% 2 50.0%
Hospitality 1028 309 30.1% 24 7.8% 15 62.5% 3 20.0%
Hospitals & Physicians Clinics 649 283 43.6% 56 19.8% 32 57.1% 8 25.0%
Insurance 359 109 30.4% 32 29.4% 28 87.5% 2 7.1%
Law Firms & Legal Services 143 48 33.6% 17 35.4% 16 94.1% 1 6.2%
Manufacturing 3022 697 23.1% 100 14.3% 65 65.0% 16 24.6%
Media & Internet 339 62 18.3% 15 24.2% 12 80.0% 0 0.0%
Metals, Minerals, and Mining 78 14 17.9% 2 14.3% 1 50.0% 0 0.0%
Organizations & Non-Profits 289 147 50.9% 19 12.9% 12 63.2% 3 25.0%
Real Estate 403 96 23.8% 11 11.5% 6 54.5% 1 16.7%
Retail 1409 374 26.5% 59 15.8% 43 72.9% 17 39.5%
Software 543 150 27.6% 27 18.0% 23 85.2% 5 21.7%
Telecommunications 214 25 11.7% 4 16.0% 4 100.0% 1 25.0%
Transportation 513 163 31.8% 10 6.1% 5 50.0% 0 0.0%
NA 210 3 1.4% 3 100.0% 1 33.3% 0 0.0%

C Level DEI Contacts by Structure

C Level DEI by Structure
Structure Accounts Outreached_Accounts Account_Pen_Outreach Visits Outreach_to_Visit Pipeline_Units VtE Units Eval_to_Unit
Private 11575 2398 20.7% 253 10.6% 168 66.4% 35 20.8%
Public 2469 1257 50.9% 274 21.8% 198 72.3% 40 20.2%

DEI Contacts by Hierarchy

companies are structured hierarchically, like schools. Except, the industries might vary and therefore require different PDEs. What we see below is that most companies are at the top of the hierarchy. But there are quite a few who are subsidiaries and who do have a C Level DEI contact. That makes for a convoluted GTM and might need further investigation as we get more contracts, to better understand how C Level DEI contacts at children accounts impact other companies in the portfolio.

C Level DEI by Hierarchy
HierarchyTop Accounts Outreached_Accounts Account_Pen_Outreach Visits Outreach_to_Visit Pipeline_Units VtE Units Eval_to_Unit
0 3349 763 22.8% 96 12.6% 68 70.8% 14 20.6%
1 10695 2892 27.0% 431 14.9% 298 69.1% 61 20.5%

Visits by Industry

A lot we can look at in the below table. A general question I cannot answer revolves around outreach penetration. I think it is tied up in viable contacts at the accounts, which in turn

  1. You see some peaks and valleys when we look at the distribution by industry: Manufacturing, Retail, & Hospitals account for 40%+ of visits.

  2. Hospitality and Business Services are particularly low penetration given their market size.

  3. Insurance, Software, and Finance have relatively higher penetration, albeit not the largest markets.

Visits by Industry, sorted by Account Volume
Industry Accounts Contacts_Outreached Outreached_Accounts Percent_Accts_Outreached Visits Percent_Visited Outreach_to_Visit Pipeline_Units VtE Units Eval_to_Unit
Manufacturing 3535 1586 748 21.2% 124 3.5% 16.6% 81 65.3% 22 27.2%
Business Services 2347 697 411 17.5% 68 2.9% 16.5% 49 72.1% 4 8.2%
Retail 1649 820 399 24.2% 72 4.4% 18.0% 52 72.2% 18 34.6%
Hospitality 1194 544 326 27.3% 31 2.6% 9.5% 20 64.5% 3 15.0%
Finance 834 520 267 32.0% 44 5.3% 16.5% 33 75.0% 5 15.2%
Hospitals & Physicians Clinics 833 555 296 35.5% 61 7.3% 20.6% 36 59.0% 8 22.2%
Construction 756 291 173 22.9% 21 2.8% 12.1% 14 66.7% 3 21.4%
Software 693 411 199 28.7% 51 7.4% 25.6% 43 84.3% 9 20.9%
Government 661 280 165 25.0% 23 3.5% 13.9% 11 47.8% 2 18.2%
Transportation 598 311 174 29.1% 11 1.8% 6.3% 6 54.5% 1 16.7%
Energy, Utilities & Waste 515 278 165 32.0% 26 5.0% 15.8% 18 69.2% 4 22.2%
Real Estate 469 206 100 21.3% 13 2.8% 13.0% 8 61.5% 1 12.5%
Insurance 431 265 125 29.0% 39 9.0% 31.2% 33 84.6% 4 12.1%
Organizations & Non-Profits 422 331 201 47.6% 51 12.1% 25.4% 29 56.9% 5 17.2%
Media & Internet 410 160 70 17.1% 19 4.6% 27.1% 14 73.7% 1 7.1%
Consumer Services 337 21 15 4.5% 2 0.6% 13.3% 2 100.0% 0 0.0%
Healthcare Services 318 104 61 19.2% 17 5.3% 27.9% 9 52.9% 3 33.3%
Telecommunications 268 65 32 11.9% 7 2.6% 21.9% 6 85.7% 1 16.7%
NA 240 36 27 11.2% 24 10.0% 88.9% 14 58.3% 2 14.3%
Law Firms & Legal Services 186 91 51 27.4% 21 11.3% 41.2% 20 95.2% 2 10.0%
Agriculture 134 33 24 17.9% 2 1.5% 8.3% 1 50.0% 0 0.0%
Metals, Minerals, and Mining 96 22 15 15.6% 2 2.1% 13.3% 1 50.0% 0 0.0%
Education 44 19 11 25.0% 1 2.3% 9.1% 1 100.0% 0 0.0%
Holding Companies & Conglomerates 41 23 11 26.8% 6 14.6% 54.5% 4 66.7% 2 50.0%
Energy,Utilities & Waste 2 2 2 100.0% 2 100.0% 100.0% 1 50.0% 0 0.0%
Not For Profit 2 3 2 100.0% 2 100.0% 100.0% 2 100.0% 0 0.0%
Organizations 2 3 2 100.0% 2 100.0% 100.0% 0 0.0% 0 NaN%
Real Estate and Rental and Leasing 2 4 2 100.0% 2 100.0% 100.0% 0 0.0% 0 NaN%
Biotechnology 1 3 1 100.0% 1 100.0% 100.0% 1 100.0% 0 0.0%
Consulting 1 1 1 100.0% 1 100.0% 100.0% 1 100.0% 0 0.0%
Food & Beverages 1 0 0 0.0% 1 100.0% Inf% 1 100.0% 0 0.0%
Other Services(except Public Admin) 1 1 1 100.0% 1 100.0% 100.0% 0 0.0% 0 NaN%
Retail Trade 1 2 1 100.0% 1 100.0% 100.0% 0 0.0% 0 NaN%

Visits By DEI C Level Presence

  1. I think we can definitively say that if there is a C level DEI person at a firm, we have a significantly higher probability of securing a visit.

  2. BUT NOTE, C Level DEIs live in only 5% of your market.

Visits bv DEI C Level Presence
DEI_C_Level_Binary Accounts Outreached_Accounts Account_Pen_Outreach Visits Outreach_to_Visit Pipeline_Units VtE Units Eval_to_Unit
0 16180 3363 20.8% 530 15.8% 345 65.1% 68 19.7%
1 844 715 84.7% 219 30.6% 166 75.8% 32 19.3%

DEI Contacts - which Industries?

Visits by DEI C Level & Industry
Industry Accounts Outreached_Accounts Account_Pen_Outreach Visits Outreach_to_Visit Pipeline_Units VtE Units Eval_to_Unit
Hospitals & Physicians Clinics 110 103 93.6% 32 31.1% 21 65.6% 5 23.8%
Manufacturing 107 86 80.4% 32 37.2% 26 81.2% 9 34.6%
Business Services 93 81 87.1% 24 29.6% 19 79.2% 1 5.3%
Government 86 73 84.9% 10 13.7% 5 50.0% 1 20.0%
Finance 82 60 73.2% 18 30.0% 17 94.4% 3 17.6%
Organizations & Non-Profits 57 53 93.0% 16 30.2% 10 62.5% 1 10.0%
Software 44 26 59.1% 12 46.2% 11 91.7% 2 18.2%
Retail 37 35 94.6% 13 37.1% 9 69.2% 2 22.2%
Insurance 35 31 88.6% 15 48.4% 14 93.3% 1 7.1%
Law Firms & Legal Services 35 24 68.6% 11 45.8% 10 90.9% 2 20.0%
Energy, Utilities & Waste 34 32 94.1% 11 34.4% 8 72.7% 2 25.0%
Hospitality 24 24 100.0% 5 20.8% 3 60.0% 2 66.7%
Media & Internet 24 21 87.5% 9 42.9% 7 77.8% 1 14.3%
Transportation 21 20 95.2% 3 15.0% 2 66.7% 0 0.0%
Construction 18 16 88.9% 2 12.5% 1 50.0% 0 0.0%
Telecommunications 12 8 66.7% 2 25.0% 2 100.0% 0 0.0%
Real Estate 7 7 100.0% 1 14.3% 0 0.0% 0 NaN%
Healthcare Services 6 4 66.7% 2 50.0% 1 50.0% 0 0.0%
Education 4 4 100.0% 0 0.0% 0 NaN% 0 NaN%
Agriculture 3 3 100.0% 0 0.0% 0 NaN% 0 NaN%
Holding Companies & Conglomerates 2 2 100.0% 1 50.0% 0 0.0% 0 NaN%
NA 2 1 50.0% 0 0.0% 0 NaN% 0 NaN%
Metals, Minerals, and Mining 1 1 100.0% 0 0.0% 0 NaN% 0 NaN%

Looking at DEI Contact Volume in SF

Besides looking at C-Level contacts, we can try to get at commitment / size of DEI at a firm by looking at all of the DEI contacts we have in sF.

In this case:

  1. 2+ DEI contacts at an account is a positive indicator.

  2. Even having 1 DEI contact is much better than having none.

  3. We still get visits at firms without a DEI contact, but our penetration needs to be much better here.

Visits by # of DEI Contacts in SF
DEI_Contacts_Bin Accounts Outreached_Accounts Account_Pen_Outreach Visits Outreach_to_Visit Pipeline_Units VtE Units Eval_to_Unit
0 14017 1714 12.2% 123 7.2% 71 57.7% 6 8.5%
1 1527 1065 69.7% 219 20.6% 140 63.9% 23 16.4%
2 648 561 86.6% 154 27.5% 117 76.0% 21 17.9%
3 325 288 88.6% 77 26.7% 52 67.5% 14 26.9%
4 171 148 86.5% 46 31.1% 31 67.4% 9 29.0%
5+ 336 302 89.9% 130 43.0% 100 76.9% 27 27.0%

Total Contacts

Visits by # of Total Contacts in SF
Total_Contacts_Bin Accounts Outreached_Accounts Account_Pen_Outreach Visits Outreach_to_Visit Pipeline_Units VtE Units Eval_to_Unit
1 2910 940 32.3% 88 9.4% 50 56.8% 2 4.0%
2 1468 761 51.8% 95 12.5% 63 66.3% 4 6.3%
3 873 566 64.8% 73 12.9% 45 61.6% 5 11.1%
4 573 405 70.7% 71 17.5% 50 70.4% 8 16.0%
5+ 1731 1373 79.3% 408 29.7% 292 71.6% 74 25.3%
NA 9469 33 0.3% 14 42.4% 11 78.6% 7 63.6%

Geographic Considerations

  1. You’d expect industry to be correlated pretty closely with state (e.g. finance & NY), so that might be driving the below.

  2. Still, you’d say NY, CA, TX are producing a lot of visits, but FL - No Bueno.

Visits by State
Primary_State Accounts Outreached_Accounts Account_Pen_Outreach Visits Outreach_to_Visit Pipeline_Units VtE Units Eval_to_Unit
CA 2261 447 19.8% 90 20.1% 61 67.8% 12 19.7%
TX 1391 338 24.3% 42 12.4% 28 66.7% 5 17.9%
NY 1259 401 31.9% 102 25.4% 73 71.6% 14 19.2%
FL 1008 162 16.1% 16 9.9% 11 68.8% 3 27.3%
IL 749 234 31.2% 40 17.1% 26 65.0% 5 19.2%
PA 636 169 26.6% 31 18.3% 21 67.7% 3 14.3%
OH 573 136 23.7% 19 14.0% 15 78.9% 1 6.7%
GA 571 137 24.0% 17 12.4% 11 64.7% 1 9.1%
MA 549 164 29.9% 46 28.0% 33 71.7% 8 24.2%
NJ 544 120 22.1% 35 29.2% 23 65.7% 8 34.8%
MI 467 86 18.4% 9 10.5% 6 66.7% 2 33.3%
NC 445 126 28.3% 15 11.9% 8 53.3% 0 0.0%
VA 433 109 25.2% 29 26.6% 21 72.4% 5 23.8%
WA 352 93 26.4% 15 16.1% 10 66.7% 0 0.0%
MN 347 77 22.2% 17 22.1% 12 70.6% 1 8.3%
CO 333 83 24.9% 9 10.8% 7 77.8% 2 28.6%
MO 308 81 26.3% 12 14.8% 10 83.3% 4 40.0%
WI 304 73 24.0% 16 21.9% 11 68.8% 2 18.2%
MD 292 81 27.7% 15 18.5% 10 66.7% 3 30.0%
AZ 291 57 19.6% 7 12.3% 5 71.4% 2 40.0%
TN 290 58 20.0% 11 19.0% 6 54.5% 0 0.0%
IN 261 46 17.6% 7 15.2% 6 85.7% 2 33.3%
CT 221 62 28.1% 10 16.1% 8 80.0% 0 0.0%
DC 182 79 43.4% 15 19.0% 6 40.0% 1 16.7%
SC 178 35 19.7% 4 11.4% 2 50.0% 0 0.0%
UT 171 37 21.6% 2 5.4% 1 50.0% 1 100.0%
OR 144 34 23.6% 4 11.8% 3 75.0% 1 33.3%
KY 139 29 20.9% 7 24.1% 4 57.1% 1 25.0%
AL 134 18 13.4% 2 11.1% 0 0.0% 0 NaN%
KS 127 23 18.1% 2 8.7% 1 50.0% 0 0.0%
NV 121 19 15.7% 0 0.0% 0 NaN% 0 NaN%
IA 111 20 18.0% 4 20.0% 3 75.0% 0 0.0%
LA 110 16 14.5% 5 31.2% 2 40.0% 0 0.0%
OK 105 23 21.9% 3 13.0% 2 66.7% 1 50.0%
NE 102 27 26.5% 1 3.7% 0 0.0% 0 NaN%
AR 79 20 25.3% 3 15.0% 2 66.7% 1 50.0%
DE 68 21 30.9% 4 19.0% 1 25.0% 0 0.0%
NA 68 4 5.9% 3 75.0% 3 100.0% 1 33.3%
ID 59 9 15.3% 3 33.3% 2 66.7% 0 0.0%
NH 57 12 21.1% 1 8.3% 1 100.0% 0 0.0%
MS 44 5 11.4% 0 0.0% 0 NaN% 0 NaN%
RI 44 17 38.6% 5 29.4% 4 80.0% 1 25.0%
WV 35 5 14.3% 0 0.0% 0 NaN% 0 NaN%
HI 34 10 29.4% 0 0.0% 0 NaN% 0 NaN%
ME 34 9 26.5% 1 11.1% 0 0.0% 0 NaN%
NM 33 8 24.2% 1 12.5% 0 0.0% 0 NaN%
SD 28 3 10.7% 0 0.0% 0 NaN% 0 NaN%
ND 25 6 24.0% 1 16.7% 1 100.0% 1 100.0%
AK 23 7 30.4% 1 14.3% 0 0.0% 0 NaN%
MT 23 5 21.7% 0 0.0% 0 NaN% 0 NaN%
PR 21 0 0.0% 0 NaN% 0 NaN% 0 NaN%
VT 20 4 20.0% 0 0.0% 0 NaN% 0 NaN%
WY 18 0 0.0% 0 NaN% 0 NaN% 0 NaN%
Puerto Rico 6 1 16.7% 0 0.0% 0 NaN% 0 NaN%
0 1 0 0.0% 0 NaN% 0 NaN% 0 NaN%
IO 1 1 100.0% 0 0.0% 0 NaN% 0 NaN%

Political Considerations

Funnel by State & 2020 Election
Election_Result Accounts Outreached_Accounts Account_Pen_Outreach Visits Outreach_to_Visit Pipeline_Units VtE Units Eval_to_Unit
Dark Red 1931 388 20.1% 57 14.7% 36 63.2% 11 30.6%
Red 328 65 19.8% 7 10.8% 3 42.9% 0 0.0%
Leans Red 2075 494 23.8% 65 13.2% 46 70.8% 6 13.0%
Slight Red 1453 288 19.8% 31 10.8% 19 61.3% 3 15.8%
Slight Blue 2390 541 22.6% 80 14.8% 54 67.5% 10 18.5%
Leans Blue 438 98 22.4% 19 19.4% 13 68.4% 1 7.7%
Blue 799 200 25.0% 39 19.5% 28 71.8% 7 25.0%
Dark Blue 6719 1767 26.3% 381 21.6% 258 67.7% 53 20.5%
Funnel by State & 2020 Election
Election_Result Primary_State Accounts Outreached_Accounts Account_Pen_Outreach Visits Outreach_to_Visit Pipeline_Units VtE Units Eval_to_Unit
Dark Red AL 134 18 13.4% 2 11.1% 0 0.0% 0 NaN%
Dark Red AR 79 20 25.3% 3 15.0% 2 66.7% 1 50.0%
Dark Red ID 59 9 15.3% 3 33.3% 2 66.7% 0 0.0%
Dark Red IN 261 46 17.6% 7 15.2% 6 85.7% 2 33.3%
Dark Red KY 139 29 20.9% 7 24.1% 4 57.1% 1 25.0%
Dark Red LA 110 16 14.5% 5 31.2% 2 40.0% 0 0.0%
Dark Red MO 308 81 26.3% 12 14.8% 10 83.3% 4 40.0%
Dark Red MS 44 5 11.4% 0 0.0% 0 NaN% 0 NaN%
Dark Red MT 23 5 21.7% 0 0.0% 0 NaN% 0 NaN%
Dark Red ND 25 6 24.0% 1 16.7% 1 100.0% 1 100.0%
Dark Red NE 102 27 26.5% 1 3.7% 0 0.0% 0 NaN%
Dark Red OK 105 23 21.9% 3 13.0% 2 66.7% 1 50.0%
Dark Red SD 28 3 10.7% 0 0.0% 0 NaN% 0 NaN%
Dark Red TN 290 58 20.0% 11 19.0% 6 54.5% 0 0.0%
Dark Red UT 171 37 21.6% 2 5.4% 1 50.0% 1 100.0%
Dark Red WV 35 5 14.3% 0 0.0% 0 NaN% 0 NaN%
Dark Red WY 18 0 0.0% 0 NaN% 0 NaN% 0 NaN%
Red AK 23 7 30.4% 1 14.3% 0 0.0% 0 NaN%
Red KS 127 23 18.1% 2 8.7% 1 50.0% 0 0.0%
Red SC 178 35 19.7% 4 11.4% 2 50.0% 0 0.0%
Leans Red IA 111 20 18.0% 4 20.0% 3 75.0% 0 0.0%
Leans Red OH 573 136 23.7% 19 14.0% 15 78.9% 1 6.7%
Leans Red TX 1391 338 24.3% 42 12.4% 28 66.7% 5 17.9%
Slight Red FL 1008 162 16.1% 16 9.9% 11 68.8% 3 27.3%
Slight Red NC 445 126 28.3% 15 11.9% 8 53.3% 0 0.0%
Slight Blue AZ 291 57 19.6% 7 12.3% 5 71.4% 2 40.0%
Slight Blue GA 571 137 24.0% 17 12.4% 11 64.7% 1 9.1%
Slight Blue MI 467 86 18.4% 9 10.5% 6 66.7% 2 33.3%
Slight Blue NV 121 19 15.7% 0 0.0% 0 NaN% 0 NaN%
Slight Blue PA 636 169 26.6% 31 18.3% 21 67.7% 3 14.3%
Slight Blue WI 304 73 24.0% 16 21.9% 11 68.8% 2 18.2%
Leans Blue ME 34 9 26.5% 1 11.1% 0 0.0% 0 NaN%
Leans Blue MN 347 77 22.2% 17 22.1% 12 70.6% 1 8.3%
Leans Blue NH 57 12 21.1% 1 8.3% 1 100.0% 0 0.0%
Blue CO 333 83 24.9% 9 10.8% 7 77.8% 2 28.6%
Blue NM 33 8 24.2% 1 12.5% 0 0.0% 0 NaN%
Blue VA 433 109 25.2% 29 26.6% 21 72.4% 5 23.8%
Dark Blue CA 2261 447 19.8% 90 20.1% 61 67.8% 12 19.7%
Dark Blue CT 221 62 28.1% 10 16.1% 8 80.0% 0 0.0%
Dark Blue DC 182 79 43.4% 15 19.0% 6 40.0% 1 16.7%
Dark Blue DE 68 21 30.9% 4 19.0% 1 25.0% 0 0.0%
Dark Blue HI 34 10 29.4% 0 0.0% 0 NaN% 0 NaN%
Dark Blue IL 749 234 31.2% 40 17.1% 26 65.0% 5 19.2%
Dark Blue MA 549 164 29.9% 46 28.0% 33 71.7% 8 24.2%
Dark Blue MD 292 81 27.7% 15 18.5% 10 66.7% 3 30.0%
Dark Blue NJ 544 120 22.1% 35 29.2% 23 65.7% 8 34.8%
Dark Blue NY 1259 401 31.9% 102 25.4% 73 71.6% 14 19.2%
Dark Blue OR 144 34 23.6% 4 11.8% 3 75.0% 1 33.3%
Dark Blue RI 44 17 38.6% 5 29.4% 4 80.0% 1 25.0%
Dark Blue VT 20 4 20.0% 0 0.0% 0 NaN% 0 NaN%
Dark Blue WA 352 93 26.4% 15 16.1% 10 66.7% 0 0.0%

Closer Look at FL - because why not.

The smattering across industries doesn’t suggest an industry driven explanation.

Visits by FL
Industry N Visits Percent_Visited Pipeline_Units VtE Units Eval_to_Unit
Business Services 149 1 0.7% 0 0.0% 0 NaN%
Hospitality 124 3 2.4% 2 66.7% 0 0.0%
Manufacturing 113 1 0.9% 1 100.0% 0 0.0%
Retail 92 2 2.2% 2 100.0% 1 50.0%
Hospitals & Physicians Clinics 61 3 4.9% 2 66.7% 0 0.0%
Real Estate 53 0 0.0% 0 NaN% 0 NaN%
Construction 46 0 0.0% 0 NaN% 0 NaN%
Government 43 0 0.0% 0 NaN% 0 NaN%
Finance 39 1 2.6% 1 100.0% 0 0.0%
Insurance 37 0 0.0% 0 NaN% 0 NaN%
Transportation 35 0 0.0% 0 NaN% 0 NaN%
Consumer Services 28 0 0.0% 0 NaN% 0 NaN%
Media & Internet 28 0 0.0% 0 NaN% 0 NaN%
Healthcare Services 26 2 7.7% 0 0.0% 0 NaN%
Energy, Utilities & Waste 22 0 0.0% 0 NaN% 0 NaN%
Software 22 0 0.0% 0 NaN% 0 NaN%
Telecommunications 21 0 0.0% 0 NaN% 0 NaN%
Organizations & Non-Profits 18 0 0.0% 0 NaN% 0 NaN%
NA 17 1 5.9% 1 100.0% 0 0.0%
Law Firms & Legal Services 14 0 0.0% 0 NaN% 0 NaN%
Agriculture 11 0 0.0% 0 NaN% 0 NaN%
Holding Companies & Conglomerates 4 2 50.0% 2 100.0% 2 100.0%
Metals, Minerals, and Mining 3 0 0.0% 0 NaN% 0 NaN%
Education 2 0 0.0% 0 NaN% 0 NaN%

Marketing Engagement

Basically all of the visits are at accounts which are engaged. Making this a meaningfully, meaningless metric.

Visits by Marketing Engagement
Engaged_Binary N Visits Percent_Visited Pipeline_Units VtE Units Eval_to_Unit
0 14025 69 0.5% 40 58.0% 2 5.0%
1 2999 680 22.7% 471 69.3% 98 20.8%

Marketing Invitation - but this just overlaps with DEI contacts and is potentially less inclusive

Now we know that there are a bunch of accounts without marketing invites. And my bet is that they are smaller. Go to the Revenue Section

Visits by Marketing Invitation
Invited_Binary N Visits Percent_Visited Pipeline_Units VtE Units Eval_to_Unit
0 9771 14 0.1% 7 50.0% 0 0.0%
1 7253 735 10.1% 504 68.6% 100 19.8%

Scatterplots - Looking at Bivariate Relationships

Looking at the relationship between annual revenue and employees, we note a few data trends:

  1. Most of the data lives in the bottom left of the graph - log 5 to log 6 is about 100M to 1Bn, with 100-10000 employees.

  2. Most of the visits appear above a line that says if the ratio of your revenue to employees is below 1 (on a log scale) then we are less likely to get a visit.

  3. Above the line, the visits are relatively scattered…see next plot.

What if we look only at the companies at which we are getting visits. What does that tell us?

  1. It becomes quite apparent that we have a sweet spot: companies with between 1Bn (but really closer to 100Bn) and employees between 10K-30K

  2. We aren’t getting most of our visits in the most densely populated part of the market!

Scatterplot- Manufacturing

  1. You can see by the elliptical shape, there is a lot spread across Manufacturing.
  2. The lower left is where we see most of the density.
  3. It looks like the visits are scattered mostly toward the right end of the graph.

Where do we get manufacturing visits?

  1. 10-30K employees, 3Bn - 10Bn

Visits by Revenue Per Employee
RevperEmployee_Quartiles N Visits Percent_Visited
Q1 1 1 100.0%
Q2 2 0 0.0%
Q3 2 1 50.0%
Q4 16905 656 3.9%
NA 114 91 79.8%

Boxplots

Looking at the distribution of employees by industry and visit.

  1. What we see across all of the industries is that we tend to get visits at firms with higher number of employees.

Visits by Revenue and Industry

Similar to what we found from the above viz, we are consistently sourcing visits from

Optimal Cutoffs for Revenue

Using an algorithm, we created the optimal cuts for Revenue. We have a surprising rate for the lowest bucket on revenue, which I thought was outside of TAM. But then you get the expected behavior of uniform increases in the rate of visits as the revenue increases.

Funnel by Annual Revenue Bucketed
AnnualRevenue_Breaks Accounts Outreached_Accounts Account_Pen_Outreach Visits Outreach_to_Visit Pipeline_Units VtE Units Eval_to_Unit
0-144M 572 164 28.7% 83 50.6% 50 60.2% 8 16.0%
144M-1.4Bn 12120 1913 15.8% 201 10.5% 137 68.2% 21 15.3%
1.4Bn-5Bn 2700 1054 39.0% 168 15.9% 118 70.2% 16 13.6%
5Bn-12Bn 833 443 53.2% 108 24.4% 70 64.8% 26 37.1%
12Bn+ 759 468 61.7% 151 32.3% 109 72.2% 22 20.2%
NA 40 36 90.0% 38 105.6% 27 71.1% 7 25.9%
Revenue Buckets & Marketing Invite
Invited_Binary AnnualRevenue_Breaks Accounts Visits Percent_Visited Pipeline_Units VtE Units Eval_to_Unit
0 0-144M 328 2 0.6% 1 50.0% 0 0.0%
0 144M-1.4Bn 8157 5 0.1% 2 40.0% 0 0.0%
0 1.4Bn-5Bn 949 1 0.1% 0 0.0% 0 NaN%
0 5Bn-12Bn 205 1 0.5% 1 100.0% 0 0.0%
0 12Bn+ 127 0 0.0% 0 NaN% 0 NaN%
0 NA 5 5 100.0% 3 60.0% 0 0.0%
1 0-144M 244 81 33.2% 49 60.5% 8 16.3%
1 144M-1.4Bn 3963 196 4.9% 135 68.9% 21 15.6%
1 1.4Bn-5Bn 1751 167 9.5% 118 70.7% 16 13.6%
1 5Bn-12Bn 628 107 17.0% 69 64.5% 26 37.7%
1 12Bn+ 632 151 23.9% 109 72.2% 22 20.2%
1 NA 35 33 94.3% 24 72.7% 7 29.2%

Optimal Cutoffs for Employee Size

Again, using an algorithm to determine the optimal points to make the cutoffs, we see a surprising result at the lowest end of the employee count. Then we see the expected increasing rate for increasing company employee counts. The annual revenue cuts do a little bit better job of creating higher differentiated buckets of companies, based on visit rates.

Visits by Employees Bucketed
Employee_Breaks Accounts Visits Percent_Visited Pipeline_Units VtE Units Eval_to_Unit
0-982 116 100 86.2% 63 63.0% 8 12.7%
982-5.2K 12880 237 1.8% 168 70.9% 34 20.2%
5.2K-14K 2172 123 5.7% 92 74.8% 16 17.4%
14K-35.2K 1090 138 12.7% 89 64.5% 21 23.6%
35.2K+ 728 115 15.8% 75 65.2% 15 20.0%
NA 38 36 94.7% 24 66.7% 6 25.0%

Combining Employee Counts and Annual Revenue with Optimal Cuts

  1. In the smallest revenue and employee buckets, there are a small number of accounts at which we have crazy high penetration.

  2. A very large proportion of accounts live in the 144M to 1.4Bn revenue bucket, with 982 to 5.2K employees, and our penetration is rather poor, even if we have a decent number of visits.

Visits by Employees & Revenue Bucketed
AnnualRevenue_Breaks Employee_Breaks Accounts Visits Percent_Visited Pipeline_Units VtE Units Eval_to_Unit
0-144M 0-982 74 64 86.5% 38 59.4% 6 15.8%
0-144M 982-5.2K 468 10 2.1% 8 80.0% 1 12.5%
0-144M 5.2K-14K 14 0 0.0% 0 NaN% 0 NaN%
0-144M 14K-35.2K 7 0 0.0% 0 NaN% 0 NaN%
0-144M 35.2K+ 1 1 100.0% 0 0.0% 0 NaN%
144M-1.4Bn 0-982 28 23 82.1% 15 65.2% 0 0.0%
144M-1.4Bn 982-5.2K 11319 145 1.3% 100 69.0% 17 17.0%
144M-1.4Bn 5.2K-14K 682 27 4.0% 17 63.0% 3 17.6%
144M-1.4Bn 14K-35.2K 72 2 2.8% 1 50.0% 0 0.0%
144M-1.4Bn 35.2K+ 18 3 16.7% 3 100.0% 0 0.0%
1.4Bn-5Bn 0-982 3 3 100.0% 1 33.3% 0 0.0%
1.4Bn-5Bn 982-5.2K 949 54 5.7% 42 77.8% 8 19.0%
1.4Bn-5Bn 5.2K-14K 1177 61 5.2% 46 75.4% 7 15.2%
1.4Bn-5Bn 14K-35.2K 488 46 9.4% 25 54.3% 1 4.0%
1.4Bn-5Bn 35.2K+ 83 4 4.8% 4 100.0% 0 0.0%
5Bn-12Bn 982-5.2K 89 14 15.7% 10 71.4% 6 60.0%
5Bn-12Bn 5.2K-14K 219 23 10.5% 20 87.0% 5 25.0%
5Bn-12Bn 14K-35.2K 355 49 13.8% 31 63.3% 10 32.3%
5Bn-12Bn 35.2K+ 169 21 12.4% 8 38.1% 5 62.5%
12Bn+ 0-982 2 2 100.0% 2 100.0% 1 50.0%
12Bn+ 982-5.2K 50 9 18.0% 4 44.4% 1 25.0%
12Bn+ 5.2K-14K 79 12 15.2% 9 75.0% 1 11.1%
12Bn+ 14K-35.2K 167 40 24.0% 32 80.0% 10 31.2%
12Bn+ 35.2K+ 455 84 18.5% 58 69.0% 9 15.5%
NA 0-982 9 8 88.9% 7 87.5% 1 14.3%
NA 982-5.2K 5 5 100.0% 4 80.0% 1 25.0%
NA 5.2K-14K 1 0 0.0% 0 NaN% 0 NaN%
NA 14K-35.2K 1 1 100.0% 0 0.0% 0 NaN%
NA 35.2K+ 2 2 100.0% 2 100.0% 1 50.0%

Combining CDEI Presence with Revenue Cuts

Visits by CDEI & Revenue Bucketed
AnnualRevenue_Breaks Accounts C_Level_DEI Percent_C_Level Pipeline_Units VtE Units Eval_to_Unit
0-144M 572 27 14.5% 50 60.2% 8 16.0%
144M-1.4Bn 12120 295 1.7% 137 68.2% 21 15.3%
1.4Bn-5Bn 2700 220 6.2% 118 70.2% 16 13.6%
5Bn-12Bn 833 121 13.0% 70 64.8% 26 37.1%
12Bn+ 759 177 19.9% 109 72.2% 22 20.2%
NA 40 4 95.0% 27 71.1% 7 25.9%

Decision Tree

Just a sample to give the flavor of how we used it to figure out ranking of factors.

CapDB Grades + Investigation

CapDB Grades and Metrics
CapDB Accounts Contacts_Outreached Outreached_Accounts Percent_Accts_Outreached Visits Percent_Visited Outreach_to_Visit Eval Visit_to_Eval Won Visit_to_Unit
A 2074 4072 1576 76.0% 453 21.8% 28.7% 334 73.7% 78 23.4%
B 2535 2353 1408 55.5% 174 6.9% 12.4% 107 61.5% 15 14.0%
C 8206 580 574 7.0% 57 0.7% 9.9% 31 54.4% 0 0.0%
D 4208 682 519 12.3% 64 1.5% 12.3% 38 59.4% 7 18.4%
NA 1 1 1 100.0% 1 100.0% 100.0% 1 100.0% 0 0.0%
CapDB Grades and Metrics
Industry Accounts Contacts_Outreached Outreached_Accounts Percent_Accts_Outreached Visits Percent_Visited Outreach_to_Visit Eval Visit_to_Eval Won Visit_to_Unit
Business Services 1507 58 58 3.8% 5 0.3% 8.6% 3 60.0% 0 0.0%
Education 21 2 2 9.5% 0 0.0% 0.0% 0 NaN% 0 NaN%
Energy, Utilities & Waste 313 37 36 11.5% 2 0.6% 5.6% 1 50.0% 0 0.0%
Finance 390 34 34 8.7% 5 1.3% 14.7% 2 40.0% 0 0.0%
Government 372 19 20 5.4% 3 0.8% 15.0% 1 33.3% 0 0.0%
Healthcare Services 197 13 13 6.6% 3 1.5% 23.1% 2 66.7% 0 0.0%
Holding Companies & Conglomerates 28 2 2 7.1% 1 3.6% 50.0% 0 0.0% 0 NaN%
Hospitality 784 113 111 14.2% 4 0.5% 3.6% 2 50.0% 0 0.0%
Hospitals & Physicians Clinics 395 29 29 7.3% 1 0.3% 3.4% 0 0.0% 0 NaN%
Insurance 205 11 11 5.4% 3 1.5% 27.3% 2 66.7% 0 0.0%
Law Firms & Legal Services 79 1 1 1.3% 1 1.3% 100.0% 1 100.0% 0 0.0%
Manufacturing 2336 140 139 6.0% 4 0.2% 2.9% 2 50.0% 0 0.0%
Media & Internet 271 6 6 2.2% 1 0.4% 16.7% 1 100.0% 0 0.0%
Metals, Minerals, and Mining 57 5 5 8.8% 1 1.8% 20.0% 1 100.0% 0 0.0%
Organizations 1 1 1 100.0% 1 100.0% 100.0% 0 0.0% 0 NaN%
Organizations & Non-Profits 230 52 51 22.2% 9 3.9% 17.6% 3 33.3% 0 0.0%
Other Services(except Public Admin) 1 1 1 100.0% 1 100.0% 100.0% 0 0.0% 0 NaN%
Real Estate 317 28 27 8.5% 1 0.3% 3.7% 1 100.0% 0 0.0%
Software 343 23 22 6.4% 7 2.0% 31.8% 6 85.7% 0 0.0%
Telecommunications 178 2 2 1.1% 1 0.6% 50.0% 1 100.0% 0 0.0%
NA 181 3 3 1.7% 3 1.7% 100.0% 2 66.7% 0 0.0%