Turn the Internet into meaningful, structured and usable data
Sales intelligence (SI) is a collection of technologies and processes for collecting and analyzing information to help a company increase the likelihood of sales. Data is collected about sales prospects, competition, products from all over the web, news and social media sources.
This article describes how Web Scraping can help you with “collection” of Sales Intelligence and specifically how Parse can augment basic Web Scraping with the “clean up and analysis of the scraped data”.
The collection of data related to Sales is neither enough nor specific and definitely not clean.
The holy grail of Sales related data is to identify a list of customers who are ready to buy your services today and with just one call or email. But this is just the holy grail – an ideal that is largely unachievable.
The collection of data is a critical part of the overall process. Data is collected from various sources and some of it is being acquired organically as part of the interaction with a sales lead and the rest as ancillary data from other sources related to the lead – the individual, company, market, location etc
The primary channels for Sales are through
Eventually, the data about a potential customer is stored in some place in your systems.
The initial issues we face right away are that these channels all have their own data sources, formats, and data quality issues.
The inbound sales data may come through email, the website, the phone or social media or fax or the postal mail. The data for each channel may come in its own format and each source may not even have a standard format.
If the sales leads come in through a website the data could and usually is structured – it has fields for the common data elements such as Name, Email, Telephone, Company Name, etc and probably a catch-all field for comments or notes or details
Due to the ubiquity of email as a communication medium and the failure of countless attempts to topple it from its perch, what was structured data from the website can end up turning into semi-structured data if your website communicates this data to you in an email.
Email has no real structure.
However, email can be used to augment a structured data store such as a database for inbound leads or better yet a CRM system (Customer Relationship Management) which mostly uses a database in the back-end but hides the complexities for you through a prettier user interface.
Unless you are using some VOIP based smart technology, this data doesn’t really exist in most companies and definitely not in any structured format. The most you can hope for is, the call logs and some metadata about the call or the transcripts of the call.
This data has the potential to be structured but is a highly evolving source of data. Companies are trying their best to figure out how to tie this data into their traditional channels and best leverage it beyond just driving traffic to their websites and product marketing
This data can be somewhat structured if paper based forms are used and the OCR workflow in place works and works well to turn them into structured data but it does have a high error rate and in the case of postal mail it has a high latency due to the physical transportation time.
The outbound sales data may come synchronously through email campaigns or the phone or social media. The other channels such as marketing may drive leads back through asynchronous means to the website or the call center and then look more like the inbound sales data. This data again may come in its own format and each source may not even have a standard format.
Email campaigns may be a great trackable way to generate leads and eventually the response to such campaigns do lead back to the collection of data either in a reply to the email or driving the leads to a structured source of data collection such as a website.
Outbound phone call data share the same characteristics as the inbound phone data with a few additional data points.