Marketing Automation in B2B – Separating the Wheat from the Chaff

The B2B landscape in its inherent form is a complex jar of beans primarily because the initial connection needs lots of nurturing with the right mix of appropriate communication to ensure the “best weather” for sales interaction. Marketers not only have to measure outcomes right up to revenue but also find the “sweet spot” for marketing and sales to drum up the right notes.

The year 2009 and the first half of 2010 saw a marked shift towards marketing automation worldwide. All of this has helped channelize information and reach out to prospects better, yet it is pertinent to note the today’s internet savvy prospect is also armed with qualifying information about your brand, your products and your competitors as never before. To get inside the mind of the B2B buyer, marketers not only need to understand his intent from his digital body language but also ensure that automated lead generation processes in place scale up in terms of the following pertinent factors at any point of time.

• Are lead recycling programs in place for not-sales-ready leads?
• Has social media, inbound marketing and marketing automation been integrated seamlessly?
• Is your marketing communication supported by buyer-centric collaterals that help the buyer decide in your favor?
• Has your data been data washed and scrubbed clean?
• Does your web metrics provide actionable information for user profiling and conversion?
• Are sales and marketing on the same latitude to proving your prospect the best buying experience?
• Does your Social media spin influence the markets conversation about your brand effectively?
• Is your opt-in list getting fresh brew in the form of persuasive communication and supporting newsletter value?
• Is your marketing funnel measurable and process definitions flexible to innovation?
• How effective is your conversation model, does it ensure that you are at top of mind when prospects decide to bite the bait?
• Are you able to capitalize on marketing automation’s great benefit – reporting effectively and use it as a strategic tool?
• Does your data-centric marketing plans lead the way for greater customer intelligence since value of data will not be a constant ?

The above are just a few important cogs that can make or break your lead generation wheel. As marketers brace themselves to capitalize on marketing automation to enhance pipeline opportunities, trends all point to an explosive growth in marketing automation adoption. It is highly imperative that automation vendors provide more sophisticated reporting, better sales engagement processes and social media integration.

To help marketing efficiently separate the wheat from the chaff, marketing automation should not just serve as a driver of operational efficiency but more importantly enhance continuity of dialogue with prospects throughout the decision making/buying cycle at all relevant touch points.

Ultimately it is all about the harvest – the pipeline and revenue, the executive leadership would not mind how you do it.

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Analytics, its Evolution

What is ‘Analytics’ – A business intelligence application with ready to use components for data analysis, we also refer to it as ‘packaged analytics’. ‘Business Analytics’ refers to analytics applications that support analysis of data collected as part of a business process.

In similar lines we can define an analytics application that supports analysis of data collected as part of a ‘computer user’ daily activity as ‘Personal Analytics’.

Business systems evolved from the state of building custom applications to a state of configurable generic Enterprise Resource Planning (ERP) systems. Now we have configurable generic business intelligence applications called ‘Business Analytics’ which have evolved from the state of building custom business intelligence applications.

The ERP systems are designed to collect the business data where as the Business Analytics systems are designed to analyze the collated business data, so one of the key sources for a Business Analytics application is an ERP system. Data analysis is a next logical step after data collection, the ERP vendors like Oracle, SAP, Microsoft got delayed in addressing this specific requirement of data analysis. In the last two years we have seen some finer business intelligence products being acquired by the ERP vendors. Clearly the customers who are on ERP products would get a better platform that can talk to their ERP applications for data analysis.

It’s a reality that not many companies, at least the larger (>USD 500million) companies would not run their entire business in one ERP system. Consolidating all applications to one single ERP platform will not happen immediately, multiple ERP and custom applications would get added if the company grows through acquisitions, hence existence of multiple transaction systems cannot be avoided. The number of customers embracing packaged analytics from the ERP vendors will increase as the flexibility of the business analytics applications from the ERP vendors matures to accept data from other outside applications.

Logical Data Model to Packaged Reports

The business analytics applications grew step by step as following

• 1. Logical data model – as a first step towards the formation of packaged analytics, companies like IBM, Teradata provided industry specific logical data models (LDM) to help customers build their enterprise data warehouse. The LDM was based on the business process and provided the required jumpstart to enable the integration of data from multiple source systems effectively. We also have certain industry endorsed LDMs like Supply-Chain Operations Reference-model (SCOR), Public Petroleum Data Model(PPDM
• 2. Metrics definition – LDMs led to the next step of defining metrics to measure the performance of the business process. The required data for the metrics that were specific to a business process were extracted (virtually/physically) into data marts as analytic data models in a fact-dimension data model
• 3. Semantic Layers – the next step was the creation of semantic layer over the data mart to enable adhoc querying and report generation
• 4. Reports and Dashboards – then we had set of reports and dashboards delivered over the semantic layer

Still the packaged analytics are positioned as a data mart application addressing specific business process like HR or Customer Relationship, unlike ERP systems which addresses complete end to end business process of an organization…there is still more time to go for an Enterprise Analytics Application to be established.

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