the data-driven marketer's guide to cross-targeting ... · a principal at symmetrics group. in...

The Data-Driven Marketer's Guide To Cross-Targeting

Product LinesLeverage your marketing database to match prospects

to new and existing products in your portfolio

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THE DATA-DRIVEN MARKETER'S GUIDE TO CROSS-TARGETING PRODUCT LINES

What is cross-targeting and why you should be doing it now

Your marketing database may be the most valuable asset for your marketing team. However, leads come from different channels and differ in their fit and stage in the selling process. If your database is an unsegmented list of contacts, you are taking advantage only of a fraction of its potential.

This is especially true if you have a portfolio of products or if you are planning a new product launch. In this case, a well-segmented database can help you leverage your existing database to sell a new product or service. The secret for cross-targeting is matching the right prospect with the right product.

Why should you be cross-targeting? Cross-targeting helps you save money on launching new products and achieve faster time to launch. For example, if you can carve-out 10k leads for your new product launch out of your 100k-lead database, you’ll get the following benefits:

• Save money: According to Madison Logic, an average B2B lead costs anywhere from $35 to $65. Therefore, if you can carve-out 10k leads from your 100k database for a new product launch, you can save between $350k to $650k just as you start out.

• Save time: It will take you months to get 10k leads in your database. Planning and executing lead-generation campaigns from scratch is not an easy task.

Average Cost per B2B Lead

Source: MadisonLogic

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If identifying cross-targeting prospects in your database is such a great idea, why isn’t everyone doing it? The challenge is that most companies have no idea how to match the right product with the right prospect. In fact, according to MarketingSherpa, many marketing organizations fail even to do basic qualification of leads: 61% of B2B organizations send all leads directly to Sales, while only 27% of these leads are qualified. In addition, 44% of organizations don’t even verify if the record is a valid business lead before passing it to Sales. Companies that find it hard to validate business leads, are likely to find it much harder to leverage their data in order to cross-target.

For lack of better options, companies resort to ineffective and laborious ways to try and match prospects to products. Companies that did not invest in an analytical infrastructure to determine what products are the best fit for each prospect, are left with two bad options to choose from:

•Ask prospects to choose the right product themselves: Some companies are trying to get asmuch information as they can in the form itself. However, according to Marketo, the more fields you add, the lower your conversion rate is going to be. In addition, many people don’t know exactly what they are looking for and seek Sales’ advice on which product is the right fit for them. For example, Cisco’s WebEx has multiple products for online communications and collaboration. However, since finding the most suitable product is hard, Cisco decided to outsource this job to you, the prospect. Therefore, if you want to contact Sales, you need to make a decision whether you want a “collaboration solution for your company” or an “online meeting solution for your team.” If prospects do not know which option to choose, it may just cause confusion and increase form abandonment rate.

•Send Sales on fishing expeditions: Rather than tell Sales what prospects are looking for, youcan send them on fishing expeditions and see what they find. Simply send all leads directly to your business development reps for qualification, with the hope that they can figure it out. This may work for leads that have already decided what they need and are already low in the funnel. However, for all other leads, reps will need to invest time and effort, without a lot of results toshowfor.AbetteroptionwouldbetotellSalesrepsandBDRswhichproductsorservicestheprospects is likely to be interested in. This is specifically true for companies with more than one sales team, as leads need to be directed to the right team.

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Therefore, matching prospects and products for cross-targeting should not rely on long and convoluted web forms or an army of telemarketers. This can decrease conversion rate, alienate prospects that do not yet want to engage with Sales and can be cumbersome. Data-driven segmentation, on the other hand, is done in the background and is seamless both to prospects and Sales. Therefore, it is more powerful, less intrusive and can help you identify sales opportunities.

B2B companies have a lot to learn on cross-targeting from their B2C counterparts. We see cross-targeting in B2C all the time in brick-and-mortar stores. But B2C companies are getting a lot smarter in using data to cross-sell—online and offline. Here are two examples:

Hertz uses big data to cross-sell

Car rental giant Hertz, developed a sophisticated system that uses real-time predictive analytics for cross-targeting and upselling. The system gives customers custom-made offers while they interact with customer service reps at the rental counter or when getting a receipt. According to DataInformed, Hertz’s system analyzes data collected through loyalty programs and responses to past offers in order to choose which offer to give to a customer.

The sophisticated system uses predictive analytics to assess the probability for conversion. Then the system calculates the overall expected revenue by combining the profit from the offer and the overall probability to convert. For example, if a customer has 10% chance for converting on an offer worth $50 and 90% chance of converting on an offer with $20, the system will choose the latter.

When a customer has any interaction with Hertz, the company can identify what kind of promotion is likely to work best. For example, if a customer is qualified for “buy one get one free,” he may still get another offer if he refused a similar offer in the past. Another example is that if someone is not yet a member of Hertz’s loyalty program, his receipt may remind him to enroll.

How do B2C organizations cross-sell?

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Amazon’s recommendation engine

Amazon, the world’s largest online retailer, realizes that in order to increase sales, it needs to have an extremely customized online experience. The company uses the data on past purchases, items that people rated, liked or viewed in order to cross-sell other items that shoppers never knew they needed. For example, gourmet cooks will see recommendation for kitchenware while gadget enthusiasts are going to see all of the latest gadgets.

Similar algorithms are used to send the right email to the right prospect. According to Forbes, Amazon analyzes key engagement metrics such as click-through rate, open-rate and opt-out-rate in order to send email only for those customers who are likely to respond.

"It's pretty cool. Basically, if a customer qualifies for both a Books mail and a Video Games mail, the email with a higher average revenue-per-mail-sent will win out," an Amazon employee told Forbes. "Now imagine that on a scale across every single product line—customers qualifying for dozens of emails, but only the most effective one reaches their inbox."

As evident here, B2C companies are investing in sophisticated data-driven cross-targeting capabilities to drive sales. With more complex sales process, longer cycle and multiple stakeholders, it seems that B2B companies will find it hard to leverage data from cross-targeting. It’s actually the other way around, due to the vast amount of data—in databases and the web—on companies and their employees, data-driven cross-targeting can even be more effective than B2C. Here are 7 ways that you can make it happen in your organization:

1. Clean and update the database

The first step to make the DB cross-sell-ready is to cleanse and update the data, according to Michael Perla, a Principal at Symmetrics Group. In addition, it is also helpful to enrich contacts with other meaningful attributes. This means performing queries to see if you have records that have been inactive for a long period of time, merging duplicate records, updating addresses and phone numbers, etc.

How to use your database to cross-sell

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This may also be an opportunity to add additional information that could be leveraged for an integrated marketing campaign. You should also identify “must have attributes” to determine what a complete record should look like.

If you are enriching data and bringing in third party/external data, you want to be very clear on the benefit of how you are going to be using the data. You should know how this new data will be inserted into the database and used.

2. Structure your database

The second step, according to Perla, is to provide some standardized structure to make the DB more functional. Standardizing the use of data fields and customer records will be essential in helping keep the DB efficient and scalable.

To structure your database, you need: First, organize your data into usable buckets based on your business objectives. For example you may decide to structure geographies by zip-code, state or region. Company size may be small medium and large or SMB and enterprise.

Secondly, data should be structured around product interests and potential nurturing tracks. When SmartBear structured their database, they did it through organizing data around product categories. “Thefirstthingwedidwasstructurethedatabaseprimarilythroughbehavioraldata.”RecallsGaryDeAsi,SmartBear’s lead-gen expert. “We created custom fields in Marketo and used batch campaigns to tag leads and organize them into five buckets that aligned audience personas to product categories, which in our case were testing & QA, software development, web monitoring, API testing, and performance.”

3. Provide a process and infrastructure to maintain the database

The next step is making sure that the data that comes in is clean, says Perla. This means that a process and infrastructure on how to maintain the database needs to be developed and supported. This process should involve data stewards from each functional area (e.g., sales, finance, operations, etc.) that has inputs into the DB.

For example, at one company, the bookings data did not sync with the revenue data and it created focus issues. The sellers were paid on bookings and the executives were paid on revenue. The act of cross-targeting ended up being less of a focus as the teams were continually fixated on revenue recognition activities, such as invoice and ship dates. The finance department began to ‘own’ the data synchronization and it helped to free-up the sales teams to better focus on cross-targeting into the existing customer base.

4. Build personas

Now that your data is clean, enriched and structured you can start segmenting it into buyer personas. Buyer personas are a great way to match people with interests, by matching the persona’s interests and needs with a product solution.

Buyer personas are fictional representation of buyer types that have unique demographic, firmographic and product needs. Buyer personas are not one of these theoretical marketing exercises. They can be validated through analyzing campaign results and be continually refined.

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According to a research by HubSpot, using buyer-personas improves email click through rate by 16%. Sill, according to Frost and Sullivan, only 46% of B2B marketers have developed buyer personas for their prospects.

For companies in the B2B space, buyer personas tend to be more complex as compared to B2C. In B2B buying decision-making, there are two players: first, the companies that need the product or service, and second, the individual employees who act as the decision makers for the companies.

Therefore, to be successful, B2B buyer personas need to take both into account. To improve your cross-targeting and build your buyer personas for each product, think about the following attributes:

• Firmographic: The characteristics of the company that are relevant for the buying process. These may include: industry, size, number of employees, revenue growth, technologies that are deployed,suchasCRMorERP(justtonameafew).Inaddition,therearemanyotherattributesthat could be unique to your product or service, such as marketing and sales strategy, sales structure, budgets and operational structure. For example for WebEx, these variables may include multi-office company, or inside sales that may imply need for teleconferencing service. These are unique to every product, but should be taken into account.

• Demographic: These are the personal characteristics of the employee who is involved in the buying decision. These may include job title, seniority, role in the organization and education. There are also other, less tangible attributes, such as brand awareness and adoption of new technology (early or late adopter).

• Goals: Here again, there are company goals, such as make processes more efficient, save money, increase revenue. However, individual decision-makers have their own goals, such as get promoted, make their job easier, be successful and avoid failure.

• Buyer type: The buyer type represents the decision process that is made in order to reach a decision. People and company cultures differ in the way that they make decisions, and understanding which type each of your prospects belongs to is a key to success. According to Frost and Sullivan, traditionally, buyer types are divided into four groups:

- Economic buyer: Looking at the opportunity from a bottom line perspective—increasing revenue or lowering cost. Typically this buyer will have a budget and the autonomy to use it, and is in a position to make the buying decision.

- Influencer: This buyer may be the one using the product or has influence over the decision-making process, but does not have the final say or control over the budget. This buyer may act as a champion for the deal within the organization if he or she feels invested in it.

- Technical buyer: Focuses on whether the product or service fits the requirements and needs. Typically, he or she drafts the requirements document and makes sure that these requirements are met before the final decision is made. Typically, this buyer is also very involved with the details of the solution.

- Executive buyer: Typically, this buyer is not involved in the details, but sets the overall strategy that creates the need for the product or service. The executive decision-maker may not be engaged in the ins-and-outs of the deal, but does make sure that the solution meets the overall strategy.

• Interests: Each persona has unique interests and information that they would like to receive at any point in the funnel. Interest may depend on the type of buyer, goals, demographic or firmographic, but may also be product-specific, such as how will this help me with my everyday work, or will I be successful in implementing this. For more on building personas, download Mintigo’s eBook: “Creating Personas for B2B Marketing.”

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5. Update scoring models

Scoring prioritizes buyers according to their likelihood to buy in order to maximize the lifetime value to the organization. Scoring models typically take into account the data that is collected on prospects in order to identify and contact the most likely prospects.

The lead scoring model should be both very granular and reflect the most likely buyer in an actionable, data-driven manner. Typically, the data that is leveraged to score leads include the following:

•Demographic & firmographic scoring: Your updated scoring model should include the leaddefinition based on your new market MQL.

•Behavioralscoring:Thisappliestobothtonewnurturingprogramsaswellasapplyingupdatedscores to leads based on past engagement that has now become relevant (for example, previously unqualified lead that participated in a webinar).

•ContactFit:Thisscorereflectsthelikelihoodthatthisprospectwillbuyfromyou.“Fit”dependsonyour product and may include indicators such role in the organization, technical skills (for example, familiarity with JavaScript) or decision-making capacity.

There may be different scoring models for different products. For example, at SmartBear, Gary uses multiple product category scores for each lead in order to determine possible affinity that a lead has toward other products. SmartBear implemented a sophisticated lead scoring model based on behavior, demographics, and interests for each product group per lead. In other words, their scoring system scores a lead for individual products, and then wraps the lead’s product scores up to an overall product group score.

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For implementation, SmartBear is using Marketo. The company takes into account weighted, key “sales readiness” indicators such as trial downloads and activations, content downloads, web activity, target industries and job titles, and product usage. They then utilized a formula in Salesforce to identify for each lead the highest product group score from the multiple product scores in his Marketo scoring campaigns.

With multiple scoring models, SmartBear knew which product each prospect was most likely to buy. Armed with this knowledge they were able to push leads down the funnel towards the most likely product and avoid promoting products that were not a good fit.

Data-driven scoring models are as powerful as the trust they inspire in the organization. A crucial point to ensure the success of a scoring model is to align Sales and Marketing. Your Sales team must review and agree to the definition of buyer personas and of the scoring model. Specifically, Sales should agree on the scoreinwhichahand-offisbeingmadeandwhata“Sales-readylead”means.AsJackieMcElaney,BDRatMintigo says: “I am more productive when I'm able to focus my outbound on prospects that I know are high-fit. When there are too many prospects in my funnel, I tend to become less productive because I spend most of my time qualifying the prospects as opposed to engaging them.”

6. Score the same prospect for all products

While scoring is used mainly to score leads for Sales reps, it can also be used to score products for each prospect. When you score each prospect for every product, the result is that you can prioritize the products that you are going to offer each lead.

SmartBear, for example, is using scoring to cross-target—If the prospect was interested in one product but did not end up buying, the marketing team will target him for an alternate product. According to Gary, the goal for most campaigns is trial download, and asset download is only a secondary call to action.However, if there is no indication that the prospect is going to purchase the product at the end of the trial, the prospect will now be targeted to the next product in line. Thus, SmartBear marketers cross-target using the lead’s multiple product category-based scores to see if there is a better fit product within their portfolio of offerings.

“If a prospect or customer expressed interest in product A or B in the past, we know that this also increases their likelihood of becoming potential future customers of product D. This approach allows us to cross-targetandmaximizeROIforeveryleadthatisgenerated,”saysGary.

Scoring prospects both on products and product lines (like SmartBear) is a great strategy for cross-targeting. Even if a prospect did not end up buying the first, product, you can always target them with the next product with the second highest score.

7. Make your nurturing dynamic

In many cases, prospects are not going to behave as you expect them based on their persona. There are two options: either they are associated the wrong persona or the persona-product fit is not as high as you initially thought. It is important not to give up on these prospects though, and make your scoring and nurturing models dynamic in order to accommodate these prospects as well.

At SmartBear, for example, Gary’s team is able to dynamically switch between product tracks for prospects that do not respond to the initial product offering. “If a lead has already gone through a trial or sales process and has not converted– we want to continue to nurture them for that product in most cases, but

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afteracertainpointweneedtoensureourROIfromamarketingperspective. Iftheyarenotexpressingpurchasing behavior for one product, we need to alter their marketing experience in order to introduce the product(s) they will have a higher likelihood of converting for. Some of our most successful campaigns are across audiences.”

B2C companies, that have practiced cross targeting for years (placing the coffee next to the creamer) are now also leveraging data for cross-selling. B2B companies should follow suit. Cross-targeting lets you save money and save time by using leads that are already in your database as well as increase conversion rate by matching the right product with the right prospect.

There are seven key steps for cross-targeting. Cleaning, structuring and maintaining your database builds the foundations that let you use data in an effective way. Building personas and updating your scoring model and scoring each prospect for all products let’s you discover multiple cross-targeting opportunities. Last, building dynamic nurturing tracks, let’s you quickly switch offers if a prospect does not respond to your first offer.

You have scores of eager buyers hiding in your database, even for the product that you haven’t yet launched. All you need is the data and analysis to find them.

Never run a bad campaign again with Mintigo’s Customer Intelligence Platform. Using predictive analytics and big data from all over the web, we help you discover, target and engage the right prospects with the right campaigns at the right time. Mintigo works with marketers at companies like DocuSign, Autodesk and SmartBear to prioritize leads, identify cross-selling opportunities in their customer base, and target new markets to expand into. To learn more, visit http://www.mintigo.com.

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