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 11685 290 2.5%
Public 2547 297 11.7%
Unknown 4 2 50.0%
NA 3050 256 8.4%

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 1904 385 20.2% 55 14.3% 40 72.7% 8 20.0%
Construction 628 169 26.9% 15 8.9% 9 60.0% 3 33.3%
Consumer Services 280 15 5.4% 2 13.3% 2 100.0% 0 0.0%
Education 19 5 26.3% 0 0.0% 0 NaN% 0 NaN%
Energy, Utilities & Waste 449 160 35.6% 20 12.5% 15 75.0% 6 40.0%
Finance 675 269 39.9% 42 15.6% 33 78.6% 6 18.2%
Government 489 150 30.7% 14 9.3% 8 57.1% 1 12.5%
Healthcare Services 234 46 19.7% 8 17.4% 4 50.0% 0 0.0%
Holding Companies & Conglomerates 210 50 23.8% 8 16.0% 5 62.5% 2 40.0%
Hospitality 1029 323 31.4% 25 7.7% 16 64.0% 3 18.8%
Hospitals & Physicians Clinics 651 296 45.5% 59 19.9% 33 55.9% 9 27.3%
Insurance 362 114 31.5% 35 30.7% 31 88.6% 3 9.7%
Law Firms & Legal Services 143 64 44.8% 20 31.2% 17 85.0% 1 5.9%
Manufacturing 3028 742 24.5% 112 15.1% 71 63.4% 18 25.4%
Media & Internet 341 69 20.2% 18 26.1% 13 72.2% 1 7.7%
Metals, Minerals, and Mining 78 14 17.9% 2 14.3% 1 50.0% 0 0.0%
Organizations 1 1 100.0% 1 100.0% 0 0.0% 0 NaN%
Organizations & Non-Profits 289 150 51.9% 19 12.7% 12 63.2% 3 25.0%
Real Estate 404 99 24.5% 13 13.1% 7 53.8% 1 14.3%
Retail 1414 388 27.4% 65 16.8% 46 70.8% 20 43.5%
Software 545 166 30.5% 29 17.5% 24 82.8% 5 20.8%
Telecommunications 214 28 13.1% 5 17.9% 4 80.0% 1 25.0%
Transportation 514 175 34.0% 15 8.6% 10 66.7% 1 10.0%
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 11685 2564 21.9% 290 11.3% 189 65.2% 44 23.3%
Public 2547 1341 52.7% 297 22.1% 214 72.1% 48 22.4%

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 3382 829 24.5% 110 13.3% 78 70.9% 16 20.5%
1 10850 3076 28.4% 477 15.5% 325 68.1% 76 23.4%

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 3541 1731 796 22.5% 135 3.8% 17.0% 87 64.4% 25 28.7%
Business Services 2354 781 444 18.9% 79 3.4% 17.8% 54 68.4% 8 14.8%
Retail 1659 899 420 25.3% 82 4.9% 19.5% 55 67.1% 22 40.0%
Hospitality 1196 588 342 28.6% 33 2.8% 9.6% 22 66.7% 4 18.2%
Finance 840 588 291 34.6% 51 6.1% 17.5% 39 76.5% 7 17.9%
Hospitals & Physicians Clinics 838 604 314 37.5% 67 8.0% 21.3% 37 55.2% 10 27.0%
Construction 757 331 182 24.0% 23 3.0% 12.6% 15 65.2% 3 20.0%
Software 698 469 218 31.2% 56 8.0% 25.7% 44 78.6% 9 20.5%
Government 661 301 171 25.9% 24 3.6% 14.0% 12 50.0% 2 16.7%
Transportation 601 398 188 31.3% 18 3.0% 9.6% 11 61.1% 2 18.2%
Energy, Utilities & Waste 515 301 172 33.4% 26 5.0% 15.1% 18 69.2% 6 33.3%
Real Estate 473 230 107 22.6% 19 4.0% 17.8% 9 47.4% 1 11.1%
Insurance 436 305 131 30.0% 43 9.9% 32.8% 37 86.0% 6 16.2%
Organizations & Non-Profits 430 372 212 49.3% 59 13.7% 27.8% 33 55.9% 6 18.2%
Media & Internet 412 185 78 18.9% 22 5.3% 28.2% 15 68.2% 2 13.3%
Consumer Services 337 25 18 5.3% 2 0.6% 11.1% 2 100.0% 0 0.0%
Healthcare Services 321 114 66 20.6% 22 6.9% 33.3% 12 54.5% 4 33.3%
Telecommunications 268 71 35 13.1% 8 3.0% 22.9% 6 75.0% 1 16.7%
Holding Companies & Conglomerates 252 93 51 20.2% 8 3.2% 15.7% 5 62.5% 2 40.0%
NA 243 39 29 11.9% 26 10.7% 89.7% 14 53.8% 2 14.3%
Law Firms & Legal Services 188 131 71 37.8% 26 13.8% 36.6% 21 80.8% 2 9.5%
Agriculture 134 33 24 17.9% 2 1.5% 8.3% 1 50.0% 0 0.0%
Metals, Minerals, and Mining 96 23 15 15.6% 2 2.1% 13.3% 1 50.0% 0 0.0%
Education 26 16 11 42.3% 2 7.7% 18.2% 1 50.0% 0 0.0%
Organizations 3 5 3 100.0% 3 100.0% 100.0% 0 0.0% 0 NaN%
Energy,Utilities & Waste 2 3 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%
Biotechnology 1 3 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 16378 3615 22.1% 600 16.6% 378 63.0% 85 22.5%
1 909 781 85.9% 246 31.5% 178 72.4% 39 21.9%

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 115 107 93.0% 34 31.8% 21 61.8% 6 28.6%
Manufacturing 113 90 79.6% 33 36.7% 26 78.8% 10 38.5%
Business Services 98 85 86.7% 24 28.2% 19 79.2% 2 10.5%
Government 90 75 83.3% 10 13.3% 5 50.0% 1 20.0%
Finance 84 66 78.6% 22 33.3% 20 90.9% 3 15.0%
Organizations & Non-Profits 59 56 94.9% 17 30.4% 10 58.8% 1 10.0%
Software 50 34 68.0% 15 44.1% 12 80.0% 2 16.7%
Retail 41 37 90.2% 17 45.9% 12 70.6% 4 33.3%
Insurance 39 33 84.6% 17 51.5% 16 94.1% 2 12.5%
Law Firms & Legal Services 37 33 89.2% 14 42.4% 11 78.6% 2 18.2%
Energy, Utilities & Waste 34 33 97.1% 10 30.3% 7 70.0% 3 42.9%
Hospitality 27 27 100.0% 7 25.9% 3 42.9% 2 66.7%
Media & Internet 24 21 87.5% 8 38.1% 6 75.0% 1 16.7%
Transportation 23 22 95.7% 3 13.6% 2 66.7% 0 0.0%
Construction 18 17 94.4% 2 11.8% 1 50.0% 0 0.0%
Holding Companies & Conglomerates 16 13 81.2% 2 15.4% 1 50.0% 0 0.0%
Telecommunications 13 9 69.2% 3 33.3% 2 66.7% 0 0.0%
Healthcare Services 8 5 62.5% 3 60.0% 2 66.7% 0 0.0%
Real Estate 8 8 100.0% 3 37.5% 1 33.3% 0 0.0%
Agriculture 4 3 75.0% 0 0.0% 0 NaN% 0 NaN%
Education 3 3 100.0% 0 0.0% 0 NaN% 0 NaN%
NA 3 2 66.7% 1 50.0% 0 0.0% 0 NaN%
Energy,Utilities & Waste 1 1 100.0% 1 100.0% 1 100.0% 0 0.0%
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 13915 1774 12.7% 134 7.6% 70 52.2% 8 11.4%
1 1629 1091 67.0% 235 21.5% 138 58.7% 26 18.8%
2 672 572 85.1% 151 26.4% 107 70.9% 24 22.4%
3 384 349 90.9% 94 26.9% 70 74.5% 17 24.3%
4 212 177 83.5% 57 32.2% 43 75.4% 11 25.6%
5+ 475 433 91.2% 175 40.4% 128 73.1% 38 29.7%

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 2930 815 27.8% 91 11.2% 45 49.5% 2 4.4%
2 1559 703 45.1% 96 13.7% 54 56.2% 4 7.4%
3 955 562 58.8% 67 11.9% 36 53.7% 5 13.9%
4 651 407 62.5% 53 13.0% 33 62.3% 8 24.2%
5+ 2369 1863 78.6% 518 27.8% 371 71.6% 97 26.1%
NA 8823 46 0.5% 21 45.7% 17 81.0% 8 47.1%

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 2280 473 20.7% 100 21.1% 64 64.0% 14 21.9%
TX 1407 347 24.7% 47 13.5% 30 63.8% 6 20.0%
NY 1289 429 33.3% 113 26.3% 76 67.3% 19 25.0%
FL 1025 198 19.3% 21 10.6% 12 57.1% 3 25.0%
IL 766 252 32.9% 45 17.9% 28 62.2% 7 25.0%
PA 648 184 28.4% 37 20.1% 25 67.6% 5 20.0%
OH 585 146 25.0% 22 15.1% 17 77.3% 1 5.9%
GA 584 171 29.3% 22 12.9% 13 59.1% 1 7.7%
MA 556 177 31.8% 48 27.1% 35 72.9% 11 31.4%
NJ 548 126 23.0% 37 29.4% 24 64.9% 9 37.5%
MI 471 90 19.1% 9 10.0% 6 66.7% 2 33.3%
NC 452 133 29.4% 18 13.5% 11 61.1% 0 0.0%
VA 434 113 26.0% 31 27.4% 23 74.2% 5 21.7%
WA 358 98 27.4% 18 18.4% 11 61.1% 1 9.1%
MN 351 85 24.2% 19 22.4% 14 73.7% 2 14.3%
CO 336 91 27.1% 9 9.9% 7 77.8% 3 42.9%
MO 316 85 26.9% 15 17.6% 10 66.7% 4 40.0%
WI 308 77 25.0% 17 22.1% 11 64.7% 2 18.2%
MD 300 89 29.7% 19 21.3% 12 63.2% 5 41.7%
AZ 297 61 20.5% 8 13.1% 5 62.5% 2 40.0%
TN 294 63 21.4% 12 19.0% 7 58.3% 0 0.0%
IN 269 50 18.6% 9 18.0% 6 66.7% 2 33.3%
CT 226 67 29.6% 10 14.9% 9 90.0% 0 0.0%
DC 187 88 47.1% 22 25.0% 12 54.5% 2 16.7%
SC 178 35 19.7% 4 11.4% 2 50.0% 0 0.0%
UT 175 37 21.1% 3 8.1% 2 66.7% 1 50.0%
OR 145 35 24.1% 5 14.3% 4 80.0% 1 25.0%
KY 141 31 22.0% 7 22.6% 4 57.1% 1 25.0%
AL 138 22 15.9% 4 18.2% 0 0.0% 0 NaN%
KS 132 26 19.7% 2 7.7% 1 50.0% 0 0.0%
NV 124 19 15.3% 0 0.0% 0 NaN% 0 NaN%
IA 113 24 21.2% 5 20.8% 3 60.0% 0 0.0%
LA 110 16 14.5% 6 37.5% 2 33.3% 0 0.0%
OK 106 23 21.7% 3 13.0% 2 66.7% 1 50.0%
NE 103 30 29.1% 2 6.7% 1 50.0% 0 0.0%
AR 79 20 25.3% 3 15.0% 2 66.7% 1 50.0%
DE 68 23 33.8% 3 13.0% 1 33.3% 0 0.0%
NA 68 4 5.9% 3 75.0% 3 100.0% 1 33.3%
ID 61 9 14.8% 3 33.3% 2 66.7% 0 0.0%
NH 58 12 20.7% 1 8.3% 1 100.0% 0 0.0%
RI 46 17 37.0% 5 29.4% 4 80.0% 1 25.0%
MS 44 5 11.4% 0 0.0% 0 NaN% 0 NaN%
HI 36 10 27.8% 0 0.0% 0 NaN% 0 NaN%
WV 35 6 17.1% 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 24 7 29.2% 1 14.3% 0 0.0% 0 NaN%
MT 23 6 26.1% 0 0.0% 0 NaN% 0 NaN%
PR 21 0 0.0% 0 NaN% 0 NaN% 0 NaN%
VT 21 5 23.8% 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 1965 412 21.0% 68 16.5% 39 57.4% 11 28.2%
Red 334 68 20.4% 7 10.3% 3 42.9% 0 0.0%
Leans Red 2105 517 24.6% 74 14.3% 50 67.6% 7 14.0%
Slight Red 1477 331 22.4% 39 11.8% 23 59.0% 3 13.0%
Slight Blue 2432 602 24.8% 93 15.4% 60 64.5% 12 20.0%
Leans Blue 443 106 23.9% 21 19.8% 15 71.4% 2 13.3%
Blue 803 212 26.4% 41 19.3% 30 73.2% 8 26.7%
Dark Blue 6826 1889 27.7% 425 22.5% 280 65.9% 70 25.0%
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 138 22 15.9% 4 18.2% 0 0.0% 0 NaN%
Dark Red AR 79 20 25.3% 3 15.0% 2 66.7% 1 50.0%
Dark Red ID 61 9 14.8% 3 33.3% 2 66.7% 0 0.0%
Dark Red IN 269 50 18.6% 9 18.0% 6 66.7% 2 33.3%
Dark Red KY 141 31 22.0% 7 22.6% 4 57.1% 1 25.0%
Dark Red LA 110 16 14.5% 6 37.5% 2 33.3% 0 0.0%
Dark Red MO 316 85 26.9% 15 17.6% 10 66.7% 4 40.0%
Dark Red MS 44 5 11.4% 0 0.0% 0 NaN% 0 NaN%
Dark Red MT 23 6 26.1% 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 103 30 29.1% 2 6.7% 1 50.0% 0 0.0%
Dark Red OK 106 23 21.7% 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 294 63 21.4% 12 19.0% 7 58.3% 0 0.0%
Dark Red UT 175 37 21.1% 3 8.1% 2 66.7% 1 50.0%
Dark Red WV 35 6 17.1% 0 0.0% 0 NaN% 0 NaN%
Dark Red WY 18 0 0.0% 0 NaN% 0 NaN% 0 NaN%
Red AK 24 7 29.2% 1 14.3% 0 0.0% 0 NaN%
Red KS 132 26 19.7% 2 7.7% 1 50.0% 0 0.0%
Red SC 178 35 19.7% 4 11.4% 2 50.0% 0 0.0%
Leans Red IA 113 24 21.2% 5 20.8% 3 60.0% 0 0.0%
Leans Red OH 585 146 25.0% 22 15.1% 17 77.3% 1 5.9%
Leans Red TX 1407 347 24.7% 47 13.5% 30 63.8% 6 20.0%
Slight Red FL 1025 198 19.3% 21 10.6% 12 57.1% 3 25.0%
Slight Red NC 452 133 29.4% 18 13.5% 11 61.1% 0 0.0%
Slight Blue AZ 297 61 20.5% 8 13.1% 5 62.5% 2 40.0%
Slight Blue GA 584 171 29.3% 22 12.9% 13 59.1% 1 7.7%
Slight Blue MI 471 90 19.1% 9 10.0% 6 66.7% 2 33.3%
Slight Blue NV 124 19 15.3% 0 0.0% 0 NaN% 0 NaN%
Slight Blue PA 648 184 28.4% 37 20.1% 25 67.6% 5 20.0%
Slight Blue WI 308 77 25.0% 17 22.1% 11 64.7% 2 18.2%
Leans Blue ME 34 9 26.5% 1 11.1% 0 0.0% 0 NaN%
Leans Blue MN 351 85 24.2% 19 22.4% 14 73.7% 2 14.3%
Leans Blue NH 58 12 20.7% 1 8.3% 1 100.0% 0 0.0%
Blue CO 336 91 27.1% 9 9.9% 7 77.8% 3 42.9%
Blue NM 33 8 24.2% 1 12.5% 0 0.0% 0 NaN%
Blue VA 434 113 26.0% 31 27.4% 23 74.2% 5 21.7%
Dark Blue CA 2280 473 20.7% 100 21.1% 64 64.0% 14 21.9%
Dark Blue CT 226 67 29.6% 10 14.9% 9 90.0% 0 0.0%
Dark Blue DC 187 88 47.1% 22 25.0% 12 54.5% 2 16.7%
Dark Blue DE 68 23 33.8% 3 13.0% 1 33.3% 0 0.0%
Dark Blue HI 36 10 27.8% 0 0.0% 0 NaN% 0 NaN%
Dark Blue IL 766 252 32.9% 45 17.9% 28 62.2% 7 25.0%
Dark Blue MA 556 177 31.8% 48 27.1% 35 72.9% 11 31.4%
Dark Blue MD 300 89 29.7% 19 21.3% 12 63.2% 5 41.7%
Dark Blue NJ 548 126 23.0% 37 29.4% 24 64.9% 9 37.5%
Dark Blue NY 1289 429 33.3% 113 26.3% 76 67.3% 19 25.0%
Dark Blue OR 145 35 24.1% 5 14.3% 4 80.0% 1 25.0%
Dark Blue RI 46 17 37.0% 5 29.4% 4 80.0% 1 25.0%
Dark Blue VT 21 5 23.8% 0 0.0% 0 NaN% 0 NaN%
Dark Blue WA 358 98 27.4% 18 18.4% 11 61.1% 1 9.1%

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 125 5 4.0% 2 40.0% 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 2 5.1% 2 100.0% 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 1 4.8% 0 0.0% 0 NaN%
Holding Companies & Conglomerates 19 2 10.5% 2 100.0% 2 100.0%
Organizations & Non-Profits 18 0 0.0% 0 NaN% 0 NaN%
NA 18 2 11.1% 1 50.0% 0 0.0%
Law Firms & Legal Services 14 0 0.0% 0 NaN% 0 NaN%
Agriculture 11 0 0.0% 0 NaN% 0 NaN%
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 13743 62 0.5% 31 50.0% 2 6.5%
1 3544 784 22.1% 525 67.0% 122 23.2%

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 9052 12 0.1% 5 41.7% 0 0.0%
1 8235 834 10.1% 551 66.1% 124 22.5%

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 17149 736 4.3%
NA 133 108 81.2%

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 597 190 31.8% 105 55.3% 57 54.3% 12 21.1%
144M-1.4Bn 12238 2063 16.9% 234 11.3% 149 63.7% 24 16.1%
1.4Bn-5Bn 2757 1127 40.9% 188 16.7% 130 69.1% 24 18.5%
5Bn-12Bn 869 478 55.0% 118 24.7% 78 66.1% 31 39.7%
12Bn+ 784 501 63.9% 162 32.3% 115 71.0% 26 22.6%
NA 42 37 88.1% 39 105.4% 27 69.2% 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 303 3 1.0% 1 33.3% 0 0.0%
0 144M-1.4Bn 7602 6 0.1% 3 50.0% 0 0.0%
0 1.4Bn-5Bn 845 0 0.0% 0 NaN% 0 NaN%
0 5Bn-12Bn 193 0 0.0% 0 NaN% 0 NaN%
0 12Bn+ 106 0 0.0% 0 NaN% 0 NaN%
0 NA 3 3 100.0% 1 33.3% 0 0.0%
1 0-144M 294 102 34.7% 56 54.9% 12 21.4%
1 144M-1.4Bn 4636 228 4.9% 146 64.0% 24 16.4%
1 1.4Bn-5Bn 1912 188 9.8% 130 69.1% 24 18.5%
1 5Bn-12Bn 676 118 17.5% 78 66.1% 31 39.7%
1 12Bn+ 678 162 23.9% 115 71.0% 26 22.6%
1 NA 39 36 92.3% 26 72.2% 7 26.9%

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
0-982 146 129 88.4% 69 53.5% 11 15.9%
982-5.2K 12996 268 2.1% 187 69.8% 43 23.0%
5.2K-14K 2226 139 6.2% 99 71.2% 19 19.2%
14K-35.2K 1122 150 13.4% 99 66.0% 26 26.3%
35.2K+ 755 121 16.0% 78 64.5% 17 21.8%
NA 42 39 92.9% 24 61.5% 8 33.3%

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 94 82 87.2% 43 52.4% 9 20.9%
0-144M 982-5.2K 470 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 8 2 25.0% 2 100.0% 0 0.0%
0-144M 35.2K+ 1 1 100.0% 0 0.0% 0 NaN%
144M-1.4Bn 0-982 37 33 89.2% 16 48.5% 0 0.0%
144M-1.4Bn 982-5.2K 11414 167 1.5% 110 65.9% 20 18.2%
144M-1.4Bn 5.2K-14K 692 28 4.0% 17 60.7% 3 17.6%
144M-1.4Bn 14K-35.2K 73 2 2.7% 2 100.0% 0 0.0%
144M-1.4Bn 35.2K+ 21 3 14.3% 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 964 61 6.3% 48 78.7% 13 27.1%
1.4Bn-5Bn 5.2K-14K 1205 70 5.8% 49 70.0% 9 18.4%
1.4Bn-5Bn 14K-35.2K 500 50 10.0% 28 56.0% 2 7.1%
1.4Bn-5Bn 35.2K+ 85 4 4.7% 4 100.0% 0 0.0%
5Bn-12Bn 982-5.2K 93 15 16.1% 12 80.0% 7 58.3%
5Bn-12Bn 5.2K-14K 232 27 11.6% 23 85.2% 5 21.7%
5Bn-12Bn 14K-35.2K 369 54 14.6% 34 63.0% 13 38.2%
5Bn-12Bn 35.2K+ 174 21 12.1% 8 38.1% 5 62.5%
12Bn+ 0-982 3 3 100.0% 2 66.7% 1 50.0%
12Bn+ 982-5.2K 50 10 20.0% 5 50.0% 1 20.0%
12Bn+ 5.2K-14K 82 14 17.1% 10 71.4% 2 20.0%
12Bn+ 14K-35.2K 171 41 24.0% 33 80.5% 11 33.3%
12Bn+ 35.2K+ 472 90 19.1% 61 67.8% 11 18.0%
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 597 30 17.6% 57 54.3% 12 21.1%
144M-1.4Bn 12238 316 1.9% 149 63.7% 24 16.1%
1.4Bn-5Bn 2757 234 6.8% 130 69.1% 24 18.5%
5Bn-12Bn 869 135 13.6% 78 66.1% 31 39.7%
12Bn+ 784 189 20.7% 115 71.0% 26 22.6%
NA 42 5 92.9% 27 69.2% 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