Posts Tagged ‘ telemarketing lists ’

Data Brokers and the Driving Force behind the Data Economy


TARGETED DATA LISTS

 

With all the talk of data out there, who is actually using it and what are they using?  Turns out, most data used in the enterprise today comes from internal applications. That is starting to change, and the trend will accelerate.  But for now, when asked which data types were important to their firm’s overall business strategy, the majority of business intelligence users and planners cited internal sources such as transactional data from corporate apps or other customer data. Only about 1/3 of respondents reported the importance of external sources such as scientific data, partner data or other 3rd party data.

Fewer still used unstructured external data such as Twitter feeds or other social media sources. Current data sources are limited.  Yet both business and IT decision-makers recognize the need to improve their use of data:  56% of business and IT decision-makers surveyed by Forrester see improving the use of data and analytics to improve business decisions and outcomes as a top priority. And, that potentially includes expanding the use of external data… if they can find it.

Where do they go for external data?  What types of data might complement their transactional and other internal data? How can corporate strategists and market research teams identify new sources of information? Where can they find them, and how can they acquire and consume them? Can they be combined with internal data? Are the sources safe? reliable? sustainable?

We’re kicking off research here at Forrester that looks at data providers and enablers in the new data economy. Some are veterans of the data industry like Lexis Nexis  or Dunn & Bradstreet.  But others are new players who have entered the market to help facilitate the exchange and use of data.

For example, Enigma is a new search and discovery platform for public data – note they do not say “open data” but rather any data that is available to the public whether enterprise data, scientific data, academic data or open government data.  They describe the “public data paradox” in which data is out there and available but not accessible.  Public data remains in diverse formats – although that will slowly change with new government mandates for API access – and is not yet indexed and searchable.  As they put it “you are limited to the data you know about” and you can’t see the connections among different data sets.  At Enigma, they have built an infrastructure for acquiring, indexing and searching public data.  It makes it easy to find data on a particular subject, and most importantly find data sets you didn’t even know about. As they say, “a lot of people have been pioneering how we analyze the world with data” but what was missing was how to find that data.  They want to be the “Google” for public data.

Another example on my side of the pond is Data Publica. Like Enigma, they help businesses acquire data, yet with less emphasis on self-service or a “Google” approach.  Data Publica will help identify data sources, will extract and transform the raw data into a usable structure, and will deliver data as a service.  Delivery mechanisms range from dashboards and reports to data sets or streams, either as a one-off purchase or through subscription access.  Customer solutions include regional dashboards used to determine market opportunity and RFP notifications alerting potential bidders of a new request.  Small businesses who might not have the internal resources to watch the websites of multiple government agencies on a daily basis benefit from the RFP aggregation Data Publica provides by pulling data on public RFPs from over 100 sources.  Getting a timely alert that an RFP has been issued can be the key to winning the bid.

DataPublica also assists the other side of the equation, the producers/owners of public data.  In the City of Nantes, DataPublica provided guidance in the launch of the open data initiatives, advising on data structure, as well as the delivery of data through both visualizations and APIs.  Public organizations may produce data, but they face the perennial challenge of bringing it to market.

Given the skills shortages and difficulty in recruiting data expertise, enablers like Data Publica and Enigma are key to the development of the data economy. Stay tuned on more Forrester research on these enablers of the data economy.  Posted by Jennifer Belissent, Ph.D.

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Optimizing the Call Center through Improved Targeted Data Analytics


Are you confident that your call center’s lead generation activities are targeted to reach out to the prospects that are more likely to respond positively? Often times, the answer turns out to be “What is targeting?” Let’s take a look a case study featuring call center lead generation efforts for commercial banking loan products.

In this case study, among the available prospect data records, only half were contacted each month, leaving the other half of the prospect data records untouched. The initial list selection was based on annual sales/revenue, which succeeded in eliminating the poorest performing prospects. However, those prospective customers were not further prioritized for their call center representatives to focus on the best prospects.

Adding marketing analytics to the mix improved lead generation results. Here’s a snapshot of the data analysis and recommendations made with the intent to increase the lead generation conversion rate:

Added filters to the prospect data to combine any call disposition history,

Created metrics that would track and measure lead conversion data,

Introduced third party demographics into the data to determine if prospect record prioritization
based on predictive modeling could improve their lead generation rates.

This analytical approach focused on leveraging important customer/prospect data history that the client maintains for each business. The historical data they were already capturing included: call outcome detail by month and lead disposition outcomes. As with any call center, leads could not be generated until a sales rep initiated a live discussion with a decision maker or buyer.

By incorporating an estimate (score) of each business’s likelihood to generate a live contact, the sales conversion model expected performance (aka “model lift”) to improve. The resulting scores enabled ranking that was not only reflective of the best prospective businesses but also of those most likely to generate a connection to a live person (instead of voicemail, ring/no answer, wrong number, and the like).

The initial results were quite encouraging, with a projected one-year increase in profits of $1.5+ million from the lead generation efforts. While maintaining consistent staffing and call activity levels, lead referrals for this client have increased 28%. In addition, the successful close rate of those leads has improved 10% and is expected to climb higher with additional time to book pending business. While a traditional method for building a customer look-alike model or a conversion model would have enhanced results beyond random calling, additional improvements were achieved by turning call disposition data into additional insights.

This is just one method of marketing analytics you can apply to your customer data to increase ROI through your call center or sales efforts. Optimizing your customer and prospect data before reaching out and scoring your prospects based on their interaction history and likelihood to respond can create efficiencies and enable your sales force to work more effectively on targeted lead generation efforts.

 

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Paul Raca is the Vice President of Marketing Analytics at SIGMA
Marketing Group

How to Avoid the Pitfalls of Mailing & Telemarketing List Selection


The selection of proper mailing lists is one of the most critical elements of any direct marketing program. Unfortunately, too many people involved in promotional programs for their products and services do not give proper time and consideration to the selection of mailing lists. Here’s what you need to know to avoid making a mistake.

The quality of your product or service is immaterial if your message does not reach those people who are most interested in it. Likewise, your mailing package—no matter what its quality—will be ineffective if not placed in the hands of real prospects.

To plan a successful direct mail program, you—as marketing manager—must plan carefully the selection and use of mailing lists. To help you make such an important decision, I have prepared a list of common pitfalls for you to avoid.

Pitfall No. 1 – Failure to seek advice from a professional.
Too often marketers assume that they can select the best mailing lists simply by thumbing through the SRDS book, or mailing list catalog. Not necessarily so. And, since professional advice is available at no cost (list brokers earn their fees through the rental of lists), it just makes smart business sense to tap their expertise and experience before you make your list decision.

Pitfall No. 2 – Failure to check out the list broker or consultant. It is important that you select a professional list broker or consultant, not a list peddler. You want to make sure the recommendations you receive are based on knowledge and experience of similar promotions, not “guesstimations.” You should know who some of the consultant’s clients have been. Whether his list recommendations have been productive or whether he owns or manages the lists, etc.

Pitfall No. 3 – Failure to adequately test.
Too often, those going into direct marketing do not make provisions for adequate testing of lists. Testing is the key to successful direct marketing programs. Minimum tests of 5,000 to 10,000 names per list should be made in order to determine the extent of your market and feasibility of roll outs. Explore peripheral areas by testing list segments, and you will then have the highest potential of return. Rolling out to the most successful names will provide you with the highest returns.

Pitfall No. 4 – Failure to test segments within a list.
Testing a list and analyzing the results is important, but it shouldn’t stop there. Too often mail marketers fail to take a hard look at the list to find the different ways it can work for them. What are the different segments available on the list? Are most recent available? Is it possible to select those names representing multiple purchases, minimum dollar purchase amounts, full term subscribers, credit card orders, etc. These are a few of the questions you should ask, especially on those marginal lists where a rollout of the entire list would be risky.

Pitfall No. 5 – Failure to allow sufficient list delivery time.
Mailers are notorious for not allowing list companies sufficient time to process their orders. Most list brokers advice their clients to order 3-4 weeks in advance. Sometimes, turn-around is faster, but not often. By properly planning your mailing 5-6 weeks in advance, you can assure yourself of on-time delivery, with no last minute headaches.

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