Overall Context

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

First, we want to understand if there are parts of the market are more performant than others.

Second, we want to understand if there are any particular factors which increase the likelihood of a visit going to evaluation.

Data Set Description:

  1. Looking at visits from FY19 forward
  2. Only looking at Global / SAS / SASR visits
  3. Only contacts who are engaged pre-event are included. We don’t include contacts who join the opp post visit

Takeaways:

  1. Visit to Eval has gone down across all of the segments, although to varying degrees.
  2. Large P&P and Selectives (which are gaining in % of total visits) have seen pretty large decreases in visit to eval.

For Large P&P:

  1. Lower Opex schools (<195M) OR higher Opex schools with 3+ contacts tend to be associated with higher visit to eval.
  2. If we cannot get the president on the line, then a mix of VPs, CXOs, and Provosts can do the trick. Avoid Deans, AVPs, and Directors.

For Selectives:

  1. Graduation Rates appear to matter in separating the field. Lower graduation rates tend to convert well.
  2. If we go the higher Grad Rates, then typically we see lower OpEx schools convert better.
  3. Otherwise, we’ll need to construct a visit with VPs for the higher OpEx, higher Grad Rate schools.

For Regional Publics:

  1. You want to get the president involved in the visit. As well as a VP.
  2. If you cannot do 1, then focusing on smaller schools with higher OpEx tends to be successful.

For Regional Privates:

  1. Like Regional Publics, the president’s involvement is a key to success on visit to eval.
  2. The window to success narrows if the president is not involved. You end up looking at school selectivity and the range can get tight.

Visit to Eval across the FYs

At a high level, we want to understand visit to eval performance across the fiscal years to see if there is any pattern, anomalies, or errors.

We first do this at the highest level, without any demographics or other factors considered.

What we see is that Fiscal year 22 and 23 represent a decline in visit to eval from the previous years. We also note that ’20 and ’21 were much lighter Visit years. We seriously staffed up PDEs for research ’22, after having reduced staffing in ’20.

Visit to Eval by Fiscal Year
FiscalYear Visits Eval Visit_to_Eval
2019 430 288 67.0%
2020 232 151 65.1%
2021 175 114 65.1%
2022 329 175 53.2%
2023 144 77 53.5%
Total 1310 805 61.5%

Visit to Eval By Account Segment and FY

Let’s take a simple look at this fiscal year drop by account segment:

Nearly every segment is showing the same FY23 decrease in visit to eval, although Regional Private appears the most stable. Overall, the decreased eval rate in Large Public and Privates, as well as the simultaneous increase in Selective visits and decrease in Selective visit to eval, are driving a lot of the decrease.

– Large Public and Private has a very large drop off in visit to eval. Opp volume is pacing roughly ahead of last year. – Regional Private has been declining over the past 3 years, but opp volume has outpaced the drop in conversion. Drop in visit to eval is not terrible, relatively. – Regional Public: large decline in visit to eval, – Selective: increased # of visits coming from this segment. Conversion was higher in FY21 and before, however this might be selection bias as the opp volume was lower and cherrypicking could have been at work. On pace for similar Opp volume this year, with a decreased Eval rate.

Visit to Eval by Fiscal Year
AccountSegment FiscalYear Visits Eval Visit_to_Eval
Large Public & Private 2019 59 48 81.4%
Large Public & Private 2020 28 18 64.3%
Large Public & Private 2021 26 21 80.8%
Large Public & Private 2022 101 56 55.4%
Large Public & Private 2023 49 17 34.7%
Regional Private 2019 247 147 59.5%
Regional Private 2020 134 88 65.7%
Regional Private 2021 87 52 59.8%
Regional Private 2022 101 58 57.4%
Regional Private 2023 43 34 79.1%
Regional Public 2019 99 73 73.7%
Regional Public 2020 61 39 63.9%
Regional Public 2021 47 30 63.8%
Regional Public 2022 60 28 46.7%
Regional Public 2023 20 12 60.0%
Selective 2019 25 20 80.0%
Selective 2020 9 6 66.7%
Selective 2021 15 11 73.3%
Selective 2022 67 33 49.3%
Selective 2023 32 14 43.8%
NA Total 1310 805 61.5%

Large Public & Private

Decision Tree - All Years

We start with a decision tree for all of the years we have data.

Let’s walk through the nodes and what this particular tree is telling us. We start at the top and work our way down.

  1. If OpEx is below 195M, then we typically see Visit to Eval at 79%. And 31% of the data falls into this node. If Opex is above 195M, then you get to eval 52% of the time. And interestingly, if the Opex is below 195M, there is no further information to be gained by splitting these schools further.

  2. The Number of Contacts on the Sales Opportunity comes into play next. If you have more than 3, you tend to see the Opp convert to eval at a higher rate - 72%

  3. If you have fewer than 3 contacts, the path to success is generally, larger schools with higher graduation rates.

Decision Tree - FY23

This is going to be a more tempermental tree just due to the lack of data.

Essentially, this tree tells us that OpEx and the # of students are the only two factors you need to parcel up the landscape into higher and lower visit to eval rates.

Lower Opex still performs well. And higher OpEx schools will convert to eval if they have very large student bodies, or if they are medium sized with high OpEx (above median).

Forcing Algorithm to look at Contact Data!

Forcing the algorithm to use only opex, contact #, and binary fields for contact level.

Interestingly, this would say that there is a cohort of visits with the President, and that they go to eval at a pretty high rate.

If you do not have the President on the line, then it looks like you will want to have another High Level cabinet member also present. Just having the Dean / Director / AVP leads to poor outcomes.

Then, you will want a CXO or Provost also on the line OR get 4+ contacts.

President Inclusion and VtE

The dropoff in the percent of sales pursuits with presidents seems relatively natural given the increase in the # of sales opportunities.

% President by Fiscal Year
FiscalYear Visits Percent_President
2019 59 49.2%
2020 28 32.1%
2021 26 46.2%
2022 101 19.8%
2023 49 14.3%

Up until FY23, the data suggests some lift from the inclusion of a President, although a bit of a mixed bag.

VtE w/ & w/o President by Fiscal Year
President_Binary FiscalYear Visits VtE
0 2019 30 73.3%
0 2020 19 63.2%
0 2021 14 85.7%
0 2022 81 51.9%
0 2023 42 33.3%
1 2019 29 89.7%
1 2020 9 66.7%
1 2021 12 75.0%
1 2022 20 70.0%
1 2023 7 42.9%

Selective

Decision Tree - All Years

We start with a decision tree for all of the years we have data.

Let’s walk through the nodes and what this particular tree is telling us. We start at the top and work our way down.

  1. If Graduation Rate is below 82%, then we have a terrifically high visit to eval rate.

  2. If the Graduation rate is above 82%, then we want the lower opex and highest level of graduation rates -> liberal art schools?

  3. If we have high Opex, then who is on the visit appears to matter. Deans / AVP / Directors suggest lower results. And, we want the schools with the >92% grad rates.

Decision Tree Selective - FY23

Essentially, this tree tells us that highly competitive schools and schools whose acceptance rate is above 49 have not been performing well for us.

Looks like the mid-tier acceptance rate (17-49) and pretty large student size is where we are finding more success in getting to eval.

Forcing Algorithm to look at Contact Data!

Forcing the algorithm to use only opex, contact #, and binary fields for contact level.

Interestingly, this would say that there is a cohort of visits without the President, and that they go to eval at a pretty high rate.

If the President is included, then you will want a VP level contact to also join the call to increase likelihood of success.

Trend on President Inclusion and VtE

We see a precipitous decline in Presidents being included, post-FY21. Also note that we increased the # of sales opportunities by 300%.

% President by Fiscal Year
FiscalYear Visits Percent_President
2019 25 44.0%
2020 9 77.8%
2021 15 46.7%
2022 67 10.4%
2023 32 15.6%

As you can see below, having a President included does not necessarily we get to eve

VtE w/ & w/o President by Fiscal Year
President_Binary FiscalYear Visits VtE
0 2019 14 78.6%
0 2020 2 50.0%
0 2021 8 75.0%
0 2022 60 50.0%
0 2023 27 44.4%
1 2019 11 81.8%
1 2020 7 71.4%
1 2021 7 71.4%
1 2022 7 42.9%
1 2023 5 40.0%

Regional Public

Decision Tree - All Years

We start with a decision tree for all of the years we have data.

Let’s walk through the nodes and what this particular tree is telling us. We start at the top and work our way down.

  1. Looks like having the President on the line is a must for Regional Publics. And getting a VP in the room as well is very good for visit to eval.

  2. If you cannot get the President in the room, the path to success narrows down to firmographics. Smaller schools tend to convert much higher.

  3. If you are in the larger school portion of the territory, your higher OpEx schools are more likely to go to Eval.

Decision Tree Regional Public - FY23

Essentially, this tree tells us that we’ve recently been more successful at schools where there is a low grad rate, and below median OpEx.

If we go to higher grad rate schools, we’ll want the president on the line.

Forcing Algorithm to look at Contact Data!

Forcing the algorithm to use only opex, contact #, and binary fields for contact level.

Similar to the first tree, this tells us that the President being included in the visit is a key to getting to Eval.

Otherwise, you have to get the provost in the visit.

Trend on President Inclusion and VtE

Seems like the past 3 FYs have been pretty stable on % of Visits with the President.

% President by Fiscal Year
FiscalYear Visits Percent_President
2019 99 67.7%
2020 61 47.5%
2021 47 31.9%
2022 60 31.7%
2023 20 35.0%

There is a consistent and large lift from including the President on a visit.

VtE w/ & w/o President by Fiscal Year
President_Binary FiscalYear Visits VtE
0 2019 32 65.6%
0 2020 32 46.9%
0 2021 32 59.4%
0 2022 41 41.5%
0 2023 13 46.2%
1 2019 67 77.6%
1 2020 29 82.8%
1 2021 15 73.3%
1 2022 19 57.9%
1 2023 7 85.7%

Regional Private

Decision Tree - All Years

We start with a decision tree for all of the years we have data.

Let’s walk through the nodes and what this particular tree is telling us. We start at the top and work our way down.

  1. Looks like having the President on the line is beneficial for Regional Privates (10% difference in Visit to Eval).

  2. If you do get a President in the visit, above the median OpEx (150M) tends to bode well for visit to eval.

  3. If you cannot get a President involved in the visit, then it becomes a strange range of acceptance rate - seems like the 80-90% range is generally good.

Decision Tree Regional Private - FY23

This tree feels very different from the one above.

Schools with a higher graduation rate are good targets.

Lower graduation rates, smaller schools, and more selective (lower acceptance rate).

Forcing Algorithm to look at Contact Data!

Forcing the algorithm to use only opex, contact #, and binary fields for contact level.

Tree doesn’t want to split. That might mean without the other firmographic variables, there is no statistical advantage to be gained from these binary fields.

Trend on President Inclusion & Conversion

Visit volume has been decreasing in this space. FY20-22 look pretty consistent on President inclusion, while this FY seems to have dropped off a bit.

% President by Fiscal Year
FiscalYear Visits Percent_President
2019 247 64.4%
2020 134 37.3%
2021 87 39.1%
2022 101 35.6%
2023 43 23.3%

Except for FY23, we’d say there is decent lift from having a President involved, but FY23 has certainly reversed that trend with gusto.

VtE w/ & w/o President by Fiscal Year
President_Binary FiscalYear Visits VtE
0 2019 88 55.7%
0 2020 84 59.5%
0 2021 53 52.8%
0 2022 65 53.8%
0 2023 33 84.8%
1 2019 159 61.6%
1 2020 50 76.0%
1 2021 34 70.6%
1 2022 36 63.9%
1 2023 10 60.0%