What To Do With All Your Data

Introduction

Data – acquiring it, keeping track of it, keeping it clean, refreshing it – can sometimes feel like an endless loop which exists for its own sake, a task which marketers and companies do and keep doing but without a clear idea of the end goal. And, while you do need to keep a rigorous focus on data to make it relevant, you should never lose sight of its purpose: to find out about your customers, help make sales and drive revenue.

Modern B2B marketing teams, working with sales, use many techniques to maintain customer engagement throughout the buying process. The old ‘sales funnel’ no longer exists; instead, the customer journey is now treated as an ongoing course of marketing and education, which only in the final stages reaches sales outreach and a transaction.

Marketing automation (MA) is the tech that allows marketers to collect all touchpoints during the customer’s path to purchase, and the purpose of data is to fuel that tech and give marketers the information necessary to determine intent.

This whitepaper examines two crucial methods for using data to maximise impact: lead scoring and predictive analytics. Lead scoring is the perfect place for marketing/sales alignment, combining sales know-how with marketing data to qualify leads and determine which resources should be used and applied. Predictive analytics, meanwhile, blends structured and unstructured data, algorithms and other data to predict behavioural intent and provide the most forward- thinking customer insight

1. Developing Insights With Lead Scoring

Lead scoring is a methodology used to rank prospects against a scale that represents the perceived value each lead represents to the organisation. The first stage is using prospective customer data to gauge whether the prospect is valid. If the prospect is valid, the second step is ranking that prospect against others based on their perceived value. The key thing that separates it from other uses of data is the human element: data provides the information, but once it has been processed that data equates to a score determined by humans, not a computer programme.

It’s an essential truth of data that it absolutely needs human use to release its potential. Ideally, this human process should be an opportunity to combine knowledge from both marketing and sales.

The key actions for successful lead scoring:

• Setting criteria: Sales and marketing need to work together to determine what’s important to both teams in order to nurture a lead. Consistency and objectivity are absolutely vital in this process.

• Automate: Though human modelling begins the process of determining lead rankings, it’s technology that does the work of going through all the data and collecting and analysing all the contact leads you have with your offering.

• Factor in explicit and implicit criteria: ‘Explicit’ criteria are the easily accessed pieces of information used to work out whether leads are a good fit: company size, industry, products or services and so on. ‘Implicit’ refers to harder-to-track behaviours: Do they open emails? Register for webinars? Download whitepapers? If so, then these are clearly interested prospects. Do they have control over budget or authority to buy? It is uncovering this information from the mass of data that will really deliver great leads.

• Sales and marketing continued: Once this process has been completed, there needs to be further sales and marketing collaboration used to cross-reference leads in the database among departments to make sure good leads aren’t getting hammered with messages from all sides.

Using data for ranking leads will make campaigns more efficient and effective. Plus, it’s exactly the information most needed by sales. By combining external criteria such as demographics and internal criteria like behaviour analytics, marketers can determine customer intent and provide appropriate messaging at the points in which the customers are receptive to these messages. And know the perfect time to hand over to sales and complete.

2. Anticiptate With Analytics

If lead scoring lets you know what’s going on with your customers now, predictive analytics goes one step further and lets you see into the future. Predictive tools provide scientifically derived equations that forecast how your prospects might react to marketing efforts, enabling B2B marketers to serve content or actions at the ideal moment.

With the pressure on marketers to prove ROI more acute than ever, data used predicatively is a solution which allows B2B organisations do just that. Predictive analytics collects and analyses data regarding what actions a customer took on the route to purchase (e.g. opened an email, clicked a link, inserted their email address, and so on) or the actions they didn’t take to precipitate them disappearing from the funnel.

The more of this information a marketer can gather, the easier it is to predict positive outcomes (i.e. replicate successful campaign efforts) and avoid potential failures (leaving budget to be reallocated in better areas). This also, therefore, allows marketers to attribute concrete outcomes to marketing efforts and demonstrate ROI.

The major benefits for B2B incorporating predictive analytics:

• The ability to process large amounts of data and anticipate behaviour.

• Improved ROI: lower costs without decreasing business efficiency.

• Response modelling: predicting purchases, responses and cancellations under certain conditions.

• Uplift response modelling: measuring the incremental impact of direct marketing actions.

• Customer retention and determining the points where customers leave the sales process.

• Risk modelling and fraud detection, examining financial and credit information to determine whether to do business with a company.

• Data mining: analysing large amounts of data to look for patterns and systematic relationships.

Use It Wisely

B2B companies that use lead scoring and predictive analytics see superior results from their marketing campaigns: Kentico(1) estimates the effect as being more than two times as successful as mass marketing campaigns.

B2B marketers must use their data to provide the multiple touchpoints along the way to serve and inform the prospect, work effectively with the sales department, and potentially convert to customers. By combining the human-led approach of lead scoring with the cold hard facts of data-mined predictive analytics, marketers will be able to determine which customers are valid and dedicate their resources accordingly.

1. Kentico: Lead Scoring for Success (kentico.com)