Turns out that in the LATAM region, Tableau is not the preferred data visualization platform. In fact, it is not even in the top five. As shown in the chart below, among the business sites in the TUDLA database for which an indicator of business intelligence software exists, Tableau ranks 7^th. The number 1 vendor is Qlik Technologies.

This preference does appear to vary by country, but not in Tableau’s favor. In Peru, Qlik is 7x more likely to be present as compared to Tableau – the largest relative penetration in the TUDLA database. Tableau is never more likely to be present relative to Qlik. The smallest Qlik differential, among countries with sufficient data, is 2.4x in Colombia.

The TUDLA data are corroborated by an analysis of web searches conducted by Kalinax. Their infographic (shown below) indicates that in terms of impressions, Qlik is more popular than Tableau in the LATAM region. The exception is in Colombia where there are more searches for Tableau than for Qlik. Kalinax posted several analyses of Tableau and Qlik which can be found here, here and here.

You never know what you are going to find when you start to dig. You can do your own exploration of the TUDLA database free of charge here.

At the core of the TUDLA database is a 70,000+ phone verified panel. Data from this panel include firmagraphics, contact information and full technology profiles. Surrounding this core are an additional 1 million+ business locations. Associated with these expanded data are basic firmagraphics (company name, address, URL) and expanded firmagraphics, where available (employee counts, industry and revenue). Indicators of what technology may be used at these business sites are also available.

The post Tableau vs. Qlik in Latin America appeared first on KDD Analytics.

And so will tax incentives.

The Raleigh-Durham-Chapel Hill combined statistical area (CSA), with over 2 million people, is the second largest metro area in North Carolina following Charlotte to the south. It is home to 3 major universities: Duke, University of North Carolina (UNC) and North Carolina State. Duke and UNC form two of the vertices of the “Research Triangle,” an area known for a high concentration of corporate operations and research companies and startups, especially in life sciences. The third vertex is formed by North Carolina State University in Raleigh.

The Raleigh metro area was number two on the 2015 Forbes list of the best place for businesses and careers. Durham ranked number 20 while Charlotte was 14th. Companies located in the Raleigh-Durham area include heavy hitters like IBM, GlaxoSmithKline, Cisco, SAS Institute and Lenovo.

As one of the largest research parks in the world, Research Triangle Park (RTP), sits mostly within the Durham-Chapel Hill metro area. RTP employs nearly 50,000 “high-tech” workers and is home to over 250 companies including Credit Suisse, Dell, EMC and Fidelity Investments. None of these are tobacco companies.

North Carolina has been very aggressive in wooing companies to locate in the state.  Lucrative incentives have been offered including outright grants, rebates of employee withholding taxes and sales tax refunds. Raleigh-Durham-Chapel Hill has directly benefited from these incentive programs as has the Charlotte-Concord-Gastonia metro area.

So, what does this have to do with Fintech?

Two million people and a healthy economy means there’s a lot of money that needs good advice. And a healthy program incentivizing companies to locate in the state doesn’t hurt. This combination has pushed the Durham-Chapel Hill Metro to #6 in our ranking of the Top 15 List of Fintech Securities & Investments Markets on a per employee basis in 2015.

Securities & Investments sector in Durham-Chapel Hill

What does the Securities & Investments sector look like in Durham-Chapel Hill, NC? To start, as shown below, over 50% of the 2015 addressable Fintech market is due to securities brokerage. Another 27% is due to portfolio management. This is generally consistent with the South Atlantic regional average of 45% and 28% that we saw in an earlier post.

Again, our analysis uses ZIP Pointe Market Sizer. Market Sizer is our Tableau-based market sizing tool based on ZIP Code-level Census data on over 7 million private sector business locations.

In terms of business location size by sub-sector, over 70% of the locations are small (1 to 4 payroll employees). There are only 7 locations with 100 or more employees. A bit over 40% of these locations are involved with securities brokerage, another 28% are portfolio management and 19% are investment advice (see below). With respect to “hot spots” of potential Fintech spend per employee, several business locations classified as Miscellaneous Intermediation (e.g. venture capital) have the highest Fintech spend per employee of around $12,000. In the chart below we have circled the largest locations.

Another way to drill into this market is by ZIP code. The following map shows the ZIP codes in the metro area color coded for addressable market size per employee. At the ZIP Code level, addressable Fintech market size ranges from $1k to $6k per employee.

According to Wealthminder.com, the largest financial advisory firms (in terms of assets under management) are located in ZIP Codes 22701 (Amundi Smith Breeden and Verity Asset Management), 27713 (Piedmont Investment Advisors), 27703 (Gordon Asset Management) and 27707 (NCM Capital Management Group).

 

ZIP Code 27709

So, on a per business location basis as well as a total basis, there is a very heavy concentration of potential Fintech spend in ZIP Code 27709. Focusing on the distribution of sites in this ZIP Code we see that the largest locations in the metro area are also in this ZIP Code (see the chart above with the red circle). Since this ZIP Code partially covers Research Triangle Park (RTP), these large locations are likely Credit Suisse and Fidelity Investments (see the RTP company directory).

Note: The US Census source used by Market Sizer tracks only payroll employees. Independent contractors are not reflected in these numbers. Hence, the actual employment level can be higher, possibly much higher, in sectors that make extensive use of contract labor.

Fidelity Investments and JIDGs

Fidelity is one of the larger employers in the Durham-Chapel Hill metro area. Fidelity first began to operate in North Carolina in 2002 “when it acquired an employee benefits business. Four years later, the firm designated North Carolina as a regional technology center, added a data center and a customer contact center and based Fidelity Charitable in the Triangle.” wral.com 

Fidelity further expanded its presence starting in 2006 and in 2015 announced plans to add an additional 600 jobs. Projections are for 4,200 jobs by 2018, mostly at the Research Triangle Park campus.

Fidelity, as well as other business, was likely incentivized to locate and expand in North Carolina by Job Investment Development Grants or JIDGs. These state grants are a

“performance-based, discretionary incentive program that provides cash grants directly to new and expanding companies to help offset the cost of locating or expanding a facility in the state. The amount of the grant is based on a percentage of the personal income tax withholdings associated with the new jobs.” Economic Development Partnership of North Carolina.

Companies can use JIDG funds for any purpose.

As these grants are performance-based, companies receive the full incentive only after meeting certain benchmarks.  These requirements can be in the form of a certain number of jobs created and retained, generation of a targeted $ amount of gross state product and/or the generation of a certain positive net revenue amount over the life of the grant.

Recently, North Carolina has “sweetened the pot” of these incentives, with an eye towards “transformative projects”, those that invest at least $4 billion and create 5,000+ jobs (ahem…listening Amazon?).

All states have some type of incentive program to entice businesses to locate and expand in their states. North Carolina appears to be one of the more aggressive ones. And this definitely was a contributing factor to why the Durham-Chapel Hill, NC metro area is #6 on our list.  #2 Omaha powered its way there more organically it seems.

All regions are different.  And Fintech companies looking to successfully disrupt these segments will need to be able to take these differences into account.

The post Top 15 Metros – Fidelity Durham – Fintech Securities & Investments appeared first on KDD Analytics.

In regions where data are less complete, like Latin America, a descriptive approach offers a very cost-effective way to reveal what the size of the prospective market might be. In fact, such an approach may be the only feasible approach depending on fill rates in the database.

Once identified, this population of prospective sites or look-alikes can then be pulled from the database, matched with a contact database or passed to a call center for verification of firmagraphics, contact details and technology usage.

TUDLA’s Latin America database

As we discussed in a previous post, TUDLA maintains a database of over 1 million business sites in Latin America and the Caribbean. The data include basic firmagraphics (company name, address, URL) and expanded firmagraphics where available (employee counts, industry and revenue). Also, indicators of what technology may be used at these business site are available.

TUDLA also offers free access to the data through interactive Tableau dashboards. This allows users to explore the data at an aggregate level and get a sense of TUDLA’s coverage. It also allows users to perform their own descriptive look-alike analysis.

Descriptive look-alike analysis

As a simple example, suppose I am SAP and I want to know how many prospective customers there might be for an SAP branded ERP offering in Brazil. (Alternatively, I could be an SAP ERP competitor and I am looking for possible SAP customer sites for a competitive kill campaign). Consider the following screenshot from TUDLA’s interactive Tableau dashboard.

TUDLA’s database shows (lower left-hand corner) that there are 2,828 sites in Brazil that are known to have SAP ERP. And the charts show the characteristics of these sites (i.e. the employee, revenue and industry distributions).

Also in the left-hand corner is shown that there are 424,693 sites in Brazil that share the same characteristics (employee count, revenue size and industry) as these 2,828 sites known to have SAP ERP (i.e. these are SAP’s look-alikes). So, this is the size of the prospective market for SAP in Brazil.

Further refinement to a specific firmagraphic, say, revenue size, will more narrowly focus the prospective market. For example, suppose SAP is only interested in sites with enterprise revenue greater than $50m. In this case, the prospective market size is reduced to 19,144 sites, nearly 50% of which are in the manufacturing industry (see below).

Interactive visualization

Interactive visualization is an excellent way to get a comprehensive view of exactly what is in your database. Lack of coverage in firmagraphics, geographies and other business attributes, such as technology presence, can quickly become apparent.

Moreover, it can be used, as in the case of TUDLA, to examine your look-alike market. You can quickly and cost-effectively (don’t need to pay to have a predictive model built) see the size of your prospective market based on the characteristics of your current customers.

Are your sales territories aligned as they should be?  With just a few clicks, an interactive visualization can provide the answer.

For more information on providing an interactive visualization for your data, give us a shout.

KDD Analytics is providing analytical services to TUDLA.

The post Finding B2B “Look-Alikes” in Latin America appeared first on KDD Analytics.

TUDLA

Since 1999, TUDLA (Technology User Database Latin America) has maintained a 70,000+ phone verified panel of business locations across LATAM and the Caribbean. The data from this panel include firmagraphics, contacts with direct phone numbers and opt-in email addresses and full technology profiles with hardware, software, and telecom information. Using an in-region call center, TUDLA regularly interviews IT Decision-Makers across the region to collect, update, and validate the information in the database.

But as is often the case in B2B marketing, numbers matter.  So TUDLA has partnered with other data providers to expand its database beyond the 70,000 phone-verified panel.  Today, the full database covers over 1 million business sites across the LATAM region.  These expanded data include basic firmagraphics (company name, address, URL) and expanded firmagraphics where available (employee counts, industry and revenue).  Also, indicators of what technology may be used at these business sites are available.

Tableau dashboard access to data

TUDLA is offering free “access” to their data so users can see what’s available. This access is through interactive Tableau dashboards embedded in TUDLA’s website.  Users can view and interact with data aggregations (but are not able to download any data) to get a sense of TUDLA’s coverage.

So, check it out and let TUDLA know what you think.

KDD Analytics is providing analytical services to TUDLA.

The post B2B Marketing in Latin America with TUDLA appeared first on KDD Analytics.

We’ll come back to Springfield.  But first, let’s slice that $18.8b into some smaller pieces.  Nearly 84% of this market is in the banking sector; the other 16% is in the securities & investments sector.  In absolute terms, the New York metro area (unsurprisingly) leads the list at $1,090m.  Other key financial centers that make the top 15 are Boston ($191m), Chicago ($183m), Los Angeles ($172m) and San Francisco ($171m).

Normalizing market data

Focusing on total market size misses the hot spots or concentrations of “spend potential” for the segment you’re after.  And while the best targets may not even reside in the list of large markets, you can bet a lot of your competitors haven’t figured that out.

A first step in prospect targeting is to “normalize” your data.  The New York market is #1 because of the substantial number of financial companies based in the metro area.  But on a per employee basis, there are other metro areas that rank higher than New York.  To delve into a per employee analysis, we again use ZIP Pointe Market Sizer.  Market Sizer is our Tableau-based market sizing tool using ZIP Code-level Census data on over 7 million private sector business locations.

Fintech market size per employee

We limited our analysis to the 80% of metro areas with 250 or more business locations in the banking and securities & investments sectors.  This helps reduce the “noise” in our analysis and minimizes the likelihood that a metro’s estimate is unduly swayed by a few extreme business locations.  In 2015, the top 15 metro areas in terms of Fintech market size per employee are shown in the figure below.

Aside from the fact that Chicago and Los Angeles didn’t make the list, look at the concentration in the Northeast region. 10 of the top 15 are in the Washington DC – Boston corridor.  Washington DC is the largest Fintech market in terms of revenue per employee at $12,863, followed by Springfield MA ($10,848) and Worcester MA-CT ($9,094).  Rounding out the top 5 are Boston MA-NH ($8,381) and Bridgeport CT ($7,851).  New York is 6^th with $7,844 per employee.

Washington, Boston and New York are not surprising.  But what about Springfield Massachusetts?

Savings and Loans in Springfield MA

According to 2015 US Census data, there were 4,254 employees on the payrolls of banking and securities & investments companies in the Springfield MA metro area.  Roughly 40% of these employees worked at 143 savings institution locations. Savings institutions (“savings and loans”) are apparently quite popular in New England, and quite lucrative.  85% of the total Fintech market in Springfield MA is attributable to savings institutions.

Savings institutions also account for most of the Fintech addressable market in neighboring Worcester metro (71%); but only 33% of Boston’s addressable market (although still is the top sub-sector).  In the neighboring Hartford CT metro (ranked 11^th), savings institutions account for 65% of the Fintech addressable market; and in the Portland ME metro (ranked 13^th), 67%.  So, indeed, savings institutions are popular in New England.

Although caution is always warranted when drilling down too deeply into market segments (due to low counts), this heat map reveals several interesting characteristics of the Springfield metro market:

(The heat map shows per employee Fintech market size by employee size of location and sub-sector.  A darker color and larger size indicate a larger market size per employee).

• Savings Institutions have the largest Fintech market size per employee across all location sizes in which savings institutions operate;
• Fintech market size per employee for savings institutions is over $20,000, while the average for Springfield is $10,848;
• While there are many other financial institutions in the smaller groups, savings institutions dominate the largest location sizes.

Making use of this analysis

How is this information useful?  Suppose your company has a solution for savings institutions. The Springfield MA metro has 143 prospects.  It is very accessible to the rest of the major financial markets in the Northeast.  And it most likely is ignored by your competitors located in those markets.  These prospects are likely very accessible and would be a great beachhead, providing enough revenue and referenceable customers to launch your business.  Similarly, if your mission is to disrupt the savings and loan industry, then Springfield has a concentration of customers you can target.

As always, data have different stories to tell.  Revenue per employee can also be used as a metric for productivity within the segment.  If you’re selling technology to improve employee productivity, your best market would be at the low end of the range.  In this case, savings and loans in Springfield would not be the best sub-sector to target.

We’ll continue taking apart the data on Fintech and offering a few observations along the way.  If there’s an angle you’d like us to explore in a future post, let us know.

This post was written with Tom Marsh, CTO at Boulder Equity Analytics (BEA).

The post Fintech, Savings and Loans and Springfield MA appeared first on KDD Analytics.

ZIP Pointe Market Sizer offers a quick and comprehensive way for B2B marketers to estimate the relative potential size of US private sector markets.  Offered as Tableau dashboards, ZIP Pointe Market Sizer is based on the most current US Census data from over 7 million private sector employers.  Users can determine the potential size of B2B markets starting at the state level and then drill down into metropolitan areas and their constituent 5-digit ZIP Codes.

Current measures of market potential are payroll, revenue and revenue from e-commerce.  Industry detail is available at both the 2 and 3-digit NAICS level.  Nine employee size ranges further refine market potential estimates.  Trend data on employees, sites and $ metrics cover the most recent 3 years.

Assess Relative Market Potential

The best use case for ZIP Pointe Market Sizer is directional and relational guidance as to which markets may hold the highest potential for B2B offerings.  Rank sorting markets can provide insight into whether, for example, sales territories are aligned with where potential opportunity resides.

ZIP Pointe Market Sizer allows users to filter their analysis by employee size, industry and $ metric (revenue, payroll and e-commerce revenue) as well as by geography.  Trends can be filtered to show CAGR over the last 1 to 2 years.

The free version of ZIP Pointe Market Sizer is fully functional and allows users to fully interact with data spanning the entire US Census business ZIP Code file.

KDD Analytics also offers an enhanced version of ZIP Pointe Market Sizer which includes additional measures of market potential (e.g. spending on information technology), additional years of data and access to the underlying, ZIP Code level data.  We can also customize ZIP Pointe Market Sizer to include measures based on user-supplied sales as well as user-requested functionality.  Please contact us for further information.

Ready to try?  Give it spin and let us know what you think.  Access ZIP Pointe Market Sizer here.

The post Free Access to ZIP Pointe Market Sizer appeared first on KDD Analytics.

Why does information technology investment per worker matter?

While information technology (IT) investment per worker has grown over time across all industries, there clearly are industries that use IT more intensively in their production of goods and services.  Knowing how this intensity varies across industries can inform public policy decisions.  For example, the demand for labor may be impacted by the amount of IT workers have to work with.  Thus any policy (e.g. tax) targeting business investment decisions can have a differential effect on labor markets depending on the intensity with which IT is used.

Knowing how IT intensity varies across industries is also useful from a micro perspective.  How much ABC Corp located at 100 Main Street, Any Town USA spends on IT is probably unknown to B2B marketers.  However, knowing in which industry ABC Corp plays and how many employees work at the 100 Main Street location can yield a ball park estimate of how much ABC Corp can potentially spend on IT.

Such information would be quite useful for IT vendors selling into ABC Corp.

Where does one find data on IT intensity?

The big IT market research houses, such as Forrester and IDC, provide industry-level estimates of IT spending.  But usually at an aggregated industry-level (e.g. 2-digit NAICS or 1-digit SIC).  And they are unlikely to provide employee counts.

Fortunately, the US Bureau of Economic Analysis (BEA) provides estimates of investment in IT “equipment” (computers, software and communications).  While they do not provide estimates of spending on IT services (e.g. IT consulting, system integration and outsourcing), the BEA data provide insights into how much businesses are spending on the three other main components of IT.

Moreover, the BEA provides these data at the sub-industry, 3-digit NAICS level; they provide estimates of employment at this same level of disaggregation; and they provide these data every year.

What more could a data junkie want?

Information technology investment per worker (IT intensity)

IT intensity is defined as gross investment in hardware, software and communication equipment per worker.  In 2015 (the last year for which data are available), IT intensity averaged about $3,100 per US private sector worker (in 2009 $).  This is 3 times what it was in 1998.

However, this national average hides the dramatic variation across industries.  Below is an interactive dashboard which shows IT intensity expressed as an index (industry intensity/average intensity) for 2015.  Not surprisingly, the Information Services industry invested over 8 times the national average; the Agriculture industry just .03 times.

Drilling down into the Information Services industry (click on the bar chart) shows that it is the Data Processing and Broadcast and Telecommunications sub-industries that account for the lion’s share of Information Services industry investment (see the bottom chart).  Again, not too surprising.

The dashboard allows one to select the type of IT investment.  Selecting “Communications” reveals the intensity of investment in communications equipment (phone systems, internet and networking equipment).  Again, the Information Services industry leads the pack as a result of heavy investment in the Broadcast and Telecommunications sub-industries.

However, the bottom chart shows the ranking of all sub-industries (3-digit NAICS).  Interestingly, the Water Transport and Pipeline Transport industries are relatively intensive users of communications equipment.

If anyone knows why, drop us a line, we would like to know!

Other interesting findings…selecting “HW – Servers” reveals the intensity of investment in physical servers.  The top three sub-industries are Federal Reserve Banks, Rental and Leasing and Credit Intermediation.  At the 2-digit NAICS level, it is the Finance industry that is the top investor in servers, followed by the Wholesale industry.

We will leave it to the reader to play with the dashboard and uncover other interesting stories (e.g. in the Manufacturing industry, which sub-industry invests the most in storage equipment? in printers?).

Takeaway

Our takeaway is that although the “usual suspects” appear as high intensive users of IT, at the sub-industry (3-digit NAICS) level, there is wide and not always predictable variation.  Especially when drilling into the different categories of IT investment.

What is your takeaway?

In a future article, we will take up this subject again and examine in more detail how information technology investment per worker (IT intensity) has changed over time.

The post US Information Technology Investment per Worker appeared first on KDD Analytics.

Wow!

Why are buyers shying away from salespeople?  According to this same article, salespeople “tend to prioritize a sales agenda over solving a customer’s problem.”

To avoid becoming replaced by self-service solutions, salespeople need to be more than order takers.  They need to be problem solvers.

In other words, they need to be consultative sellers.

The state of B2B information technology data

This really is nothing new.  We have seen it time and time again in the B2B information technology (IT), data vendor space.

Data vendors start out with a unique data source or process for obtaining data.  Then they run into trouble.

Originally these data are sold to companies looking to get a leg up on who are their best prospects for new customers.   Eventually, other data vendors join in and these data become a commodity.  (The commoditization process has sped up in recent years with advances in computing power and source coding.)

So now what?  To survive, do data vendors need to enhance the value of their data to their customers?  Rather than just selling customers access to their data, do vendors need to provide additional value?

Some data vendors hope for a quick sale of their business or extend their lives via channel marketing.  Others double down on the data and are able to expand into other vertical markets or find new unique data.

Still others take the route of Mintigo and Domo and provide self-service online tools that let customers “slice and dice” using data visualization platforms.  But, again, the arms race is on, and these self-service/data visualization platforms are proliferating.

Back to consultative sales

There will always be companies that consume data and have the in-house expertise to process and make sense of it.  And there will always be companies that only want marketing lists and are happy to get them through a self-service portal.

But what about mid-market companies?

They do not have in-house analytics.  But they do need help in making decisions; understanding who are their existing customers; in what market segments they play and the size of their markets; solving vexing marketing and sales issues; and finding the right prospective customers.

This is where consultative sales is needed.

A consultative seller is one who starts with the customer’s need.  And works through with the customer on what could meet that need.  Ending up with what the seller can offer to…meet that need.  Rather than starting with a canned data set/solution or platform.

A challenge faced by many data vendors is that they do not have such expertise in-house.  Not only would they need sales people with such a mind- and skill-set.  But they would also need to provide marketing analytics backup, in the form of reports, case-studies, training and real-time sales support and analysis.

Alternatively, data vendors can outsource this function and partner with companies that specialize in consultative marketing analytics.  Such partners would play the role of consultative sales support and be an integral part of the data vendor’s sales and product support processes.

Technology to the rescue?

But can’t a sophisticated self-service platform play this role?  One that, for example, allows the user to input their customer data, provides a customer profile and then scores a prospective list of new customers?  Enter your credit card number here…thank you very much!

Not really.

It has been our experience that, at the very least, customer data are messy!   Moreover, mid-market customers need help in asking the correct question; using the tool to correctly answer their question; making course corrections in their analysis; and taking the tool to the next level to address new questions and market opportunities.

This requires some level of consultative support, whether in-house or outsourced.  Not only to protect against GIGO syndrome (“garbage in, garbage out”) but to provide maximum value to the customer from use of the data vendor’s data and platform/application (if applicable).

Consultative sales and marketing analytics…a key to product differentiation

Forrester predicts that 1 million B2B salespeople will lose their jobs to self-service e-commerce by 2020 if some sort of consultative sales model is not adopted.

But we would take this further and argue that self-service e-commerce itself is in danger of becoming a commodity.  And that data vendors can use consultative sales, backed by marketing analytics, to differentiate and enhance their product offering.

The post B2B Consultative Sales appeared first on KDD Analytics.

A client’s customer was using an “off the shelf” application for prospect scoring.  This application used a very limited set of firmagraphics to construct its scores.

The customer knew our client had access to a wider array of business site data.  So they asked if we could measurably improve the performance of their prospect scores.

The customer used prospect scores to identify high potential prospects (business sites) in a information technology (IT), B2B marketing list.

The state of B2B business data

There are a number of vendors which provide data on the characteristics (firmagraphics) of business sites.  Dun and Bradstreet, InfoGroup, Compass, Orb, V12, to name a few, provide data such as number of employees, industry code and revenue.

Data vendors vary in terms of how many data fields they provide, in the amount of “white space” in these fields, as well as in how many business sites they cover.  And, of course, in price!

But all data vendors are constrained by the fact that, unlike consumer demographics, business firmagraphics tend to be more limited in number.

Another set of data vendors provide “enhanced” B2B data.  In the IT B2B marketing space, vendors like HG Data and Aberdeen provide data on the presence of certain technologies and technology vendors, counts of technology (e.g. PCs) and estimates of IT spend.

Enhanced B2B data coupled with base firmagraphics yields much more fertile ground for prospect scoring than just base firmagraphics alone.

What are B2B prospect scores?

A prospect score is a number assigned to a list of non-customers to be contacted in an outreach campaign .  After sorting this list by the score, the non-customers who are the “best” prospects will rise to the top.

These “best” or “high potential” prospects typically resemble current customers.   Converting these into actual customers is the job of the sales team.  The prospect score just tells them who they should focus on first.

Data analysts typically construct prospect scores through statistical modeling of a sample of customers and non-customers and their characteristics.

Can enhanced B2B data and methodology improve prospect score lift?

Since our client had access to enhanced B2B data along with base firmagraphics, we structured a test for our client’s customer as follows:

• First, construct a prospect score using only the few firmagraphics the customer was currently using;
• Second, construct a 2nd score that used these same firmagraphics plus enhanced B2B data fields such as IT spend, technology counts and indicators of technology presence;
• Third, construct a 3rd score using all available data and paying more careful attention to data preparation and modeling techniques.

To gauge model performance we used “lift”.  In short, lift is the degree to which a score identifies more potential customers compared to using some other score or no score at all.  We discuss lift in more detail in a prior article.

Findings – base score

Six (6) firmagraphic fields support the base score:  enterprise revenue and employees, site employees and type (HQ, branch, standalone), industry and metropolitan area.  The lift chart below shows that the resulting score does identify about 75% more customers (compared to using no score) in the top decile.

But the lift chart does not exhibit a smoothly declining lift from the highest to the lowest decile.  Nor is the lift decline very pronounced.  This suggests that the “discriminatory” ability of the score (i.e. the ability to tell the difference between a customer and a non-customer site) can be improved.

How do you construct a “lift chart”?  Start with a list of companies, some are customers and some are not.  Build a model and then score this list.  Sort the list by the prospect score, from highest to lowest potential.  And break the list into deciles.  Find the actual occurrence of customers in each decile and express this occurrence in terms of an index.  Index values > 1.0 indicate the score does a better job than using no score.

Findings – enhanced score

Various site-specific data fields for presence of technologies, IT spend and technology counts, as well as the base score’s firmagraphics, support the enhanced score.  The resulting lift chart is now smoother and exhibits a more pronounced decline in lift from the highest to the lowest decile.

That is, the discriminatory power of the score has markedly improved.  In this case, by about 15% in the top decile.

Findings – complete score

The final score uses a more sophisticated data preparation and modeling methodology.  And additional firmagraphics.  This adds an additional 5% to lift in the top decile.

The lift chart now suggests that, in the top decile, the score is 2x more likely to identify a business site that is similar to a current customer (compared to no score).

Findings – summary

Another way to look at this is to consider what happens over the first 30% of the file, not just the top decile:

• A prospect score using enhanced B2B data fields in addition to base firmagraphics identifies 12% more potential customers (than using base firmagraphics alone);
• Using a more robust methodology can boost this by another 6 percentage points.

Both data and methodology matter

The take away from this exercise?  Both data and methodology matter.  But methodology will only take you so far.

The greater the number of enhanced B2B data fields available, the more likely the discriminatory power of the model will improve.  The caveat, of course, is that these data fields need to be materially different from each other.  If they are all highly correlated with each other, then additional “similar” data fields won’t matter.

Of course, your mileage may vary in terms of the impact on lift.  But augmenting your marketing data with enhanced B2B data and using an appropriate methodology to construct your prospect scores is likely to yield a positive ROI.

The post Enhanced B2B Data Can Markedly Improve Prospect Scores appeared first on KDD Analytics.

Their goal was to determine how much “lift” the prospect score provided to their outreach campaign for their information technology networking solution.  That is, they wanted to know if the score led to (and by how much) the identification of more, higher likelihood prospects than using no score at all.

Their very thoughtful test yielded a result that surpassed even our, admittedly biased, expectations.

What are B2B prospect scores?

A prospect score is a number assigned to a list of non-customers to be contacted in an outreach campaign.  When this list is sorted by this score, the non-customers who are the “best” prospects rise to the top.

That is, if the prospect score works as expected.

These “best” or “high potential” prospects typically resemble current customers.   Converting these into actual customers is the job of the sales team.  The prospect score just tells them who they should focus on first.

What is lift?

“Lift” is marketing-speak for how much a B2B prospect score sorts a list of non-customers, from highest to lowest potential, compared to some other score, approach or no method at all.  Lift is presented as a percentage, such as 133%.

Consider a list of companies, some are customers, most are not.  Now sort this list by the prospect score, from highest to lowest potential.  And break the list into deciles.  A chart like this can then show the lift attributable to a prospect score built using this list.

This chart shows that over the first 30% of the sorted list (ordered by the prospect score), 70% of the actual customers can be identified (blue line).  This yields a lift attributable to the score, compared to using no score (a random selection in this case indicated by the red line), of 133% (i.e. [(70-30)/30]*100).

Of course, the lift that will actually occur in an outreach campaign targeting only non-customers will be different (and most likely lower).  But a prospect score that performs well in testing should also perform well in practice.

The size of the lift depends not only on how well crafted the score is but also on which alternative method the score is compared.  The lift attributable to one score vs. another might be very small.  However, compared to using no method for ranking non-customers, the lift could be quite large.

Why is lift important?

B2B prospect scores which yield appreciable lift reduce the cost of outreach campaigns.

Continuing with the above example, in order to identify 70% of actual customers without using a score, 70% of the marketing list would need to be used.  But by using the score to sort the list, these same 70% can be identified by using only 30% of the list.

This yields a cost reduction of 57% as the remaining 40% of the list does not need to be contacted (i.e. [(70-30)/70]*100).

So how do we build prospect scores?

Here at KDD Analytics, we build B2B prospect scores using statistical models.

We build these models using a sample of customers coupled with a randomly-selected sample of non-customers.  The goal is to arrive at a model that maximizes the differences (“discriminates”)  between customers and non-customers.

These differences are based on a set of “explanatory” characteristics.  In B2B, information technology marketing, these are foremost firmagraphics (such as number of employees, industry sector, enterprise revenue, etc.).  But they also include measures of information technology used at the business (such as spending on computers and software, likelihood of a certain vendor presence, number of PCs, etc).

Back to the test…

The customer of our client divided a prospecting list into two samples.  One sample used our prospect score, the other did not.

Then they initiated an actual calling campaign for their networking solution using both lists.  When the two campaigns were finished, our client’s customer compared the results.

In general, the degree of lift depends on the definition of a “successful” call.  For this test, a successful call was one in which the prospect expressed a genuine interest (follow-on call scheduled) and/or requested a quote.

The results of this test were a staggering 300%+ lift from using the KDD Analytics prospect score.

Granted this is a very high lift and we have to assume that the customer calculated lift correctly.  But it is consistent with our experience in B2B, information technology marketing.

B2B prospect scores can measurably improve outreach campaign performance.

Needless to say our client’s customer was extremely impressed with these results.  So much so that they immediately renewed their contract with our client for additional data sales and analytical services.

Of course, your mileage may vary.  But using B2B prospect scores to target potential new customers can measurably improve your marketing ROI.

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