Predictive marketing without data is like a Ferrari without fuel. Here’s how to gas up

edificioTer um Ferrari é o sonho de todos os que amam o mundo automóvel. É o veículo de sonho, a marca italiana de luxo que todos querem “bater”.

Agora imagine ter um belo Ferrari e não ter combustível. Pois é. De nada serviria. Tudo isto para explicar que as ferramentas analíticas não são nada sem… dados. Principalmente dados com qualidade.

Esta foi a forma mais simples, e certeira, de explicar a importância dos dados (e a sua qualidade) no quotidiano de um departamento de marketing. Sendo que hoje os dados com que um marketeer tem de se preocupar não são apenas os internos à sua organização. Pelo contrário. A maioria da informação está alocada em bases de dados externas. Pelo que a validação desses mesmos dados é de extrema importância. De outra forma a sua qualidade é posta em causa, o que condiciona todo o processo subsequente. 

Predictive marketing without data is like a Ferrari without fuel. Here’s how to gas up

August 25, 2014 8:01 PM 

Maria Grineva, Orb Intelligence

Marketing software is becoming increasingly intelligent, with vendors building predictive algorithms to improve every aspect of demand generation and sales.

But it’s only as good as the data fueling those algorithms.

Predictive marketing tools: An overview, and Fliptop provide tools for predictive scoring of leads on the top of the empowers inside sales teams with predictions as to who and when to call to increase lead qualifications and sales. C9DxContinuum, and Aviso analyze later stages of a sales pipeline and help businesses to forecast revenue.

Customer success tools such as GainsightPreact, and Bluenose Analytics even help after the deal has been successfully closed and the lead has become a customer: They predict which of the customers is likely to churn (drop away), and they help identify upsell opportunities for happy customers.

Some of the players, for example, SalesPredict and 6Sense, provide intelligent support for the full customer lifecycle, from scoring inbound leads to predicting customer churn.

Begemoths and Marketo have recently acquired predictive marketing software companies (RelateIQ and Insightera), indicating that they are serious about data intelligence, too.

All those tools address different stages of customer life cycle but are based on a common set of principles: Assemble customer data from multiple sources, consolidate it by customer, store it in an analytical database, and run predictive models against it.

The quality of the data is crucial, and any predictive analytics engine without the right data is like a Ferrari without fuel.

What kind of data do you need?

Generally there are two kinds of data circulating inside modern marketing software systems: internal behavioral information about leads and customers, and external information about leads and the companies they represent.

Internal behavioral signals are gathered from customer relationship management (CRM) systems, email marketing systems, customer service platforms, or billing and invoicing systems. They include data points like these: Whether a lead opens marketing emails, visits the company website, downloads company whitepapers, submits questions to the support system, etc.

External information includes such factors as the lead’s job title and additional information about the lead’s company, like its employee count, job openings, web presence, social media presence, technologies installed, patents, trademarks, and more. Static external signals like firmographics (industry, location, or company size) are more important for qualification of early stage leads, while dynamic external signals, such as news about new investment round, new office opening or executive change, are more useful for monitoring late stage opportunities and managing customer success.

Where can you get more?

To collect external signals, predictive analytics vendors have built in-house data crawlers as well as licensing the data from third-party data providers. Here’s an overview of the options.

The veterans of business information — D&B and InfoUSA — mostly collect the information manually, with the help of call centers. They focus on collecting general companies’ firmographics, like location, number of employees, industry category, and contact information for key employees. But they rarely have company websites, multiple email domains, social network accounts, or other information found on the Web.

Younger companies like my startup, Orb Intelligence, crawl information on the Web and from government filings and provide it via API to marketing software vendors. Datanyze and BuiltWith crawl the web to collect data about what technologies are installed on companies’ websites. Enigma.iointegrates and indexes United States government filings. HG Data collects information about software products used by companies and provides competitive intelligence data for the technology industry.ZoomInfo and focus on collecting employees’ contact information.

As the marketing software ecosystem matures and defines its requirements for business information, we are likely to see business information providers grow into data platforms for marketing applications. This in turn would free marketing software vendors from the burden of data collection and maintenance, letting them dive deeper into predictive analytics and specialize their products.

Maria Grineva is a co-founder/scientist of Orb Intelligence, a company that provides business information for marketing software vendors and B2B marketing agencies.

 Semhar Systems via Predictive marketing without data is like a Ferrari without fuel. Here’s how to gas up | VentureBeat | Marketing | by Maria Grineva, Orb Intelligence.

One thought on “Predictive marketing without data is like a Ferrari without fuel. Here’s how to gas up

  1. Most efficient and time saving way is to use prospecting software like Datanyze, AeroLeads etc with Rapportive to check external information as it speeds up the work and do a lot more than just verifying emails.

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