How to convert donor’s into consistent supporters of the charity?

As we know ‘Donor’ is someone who financially aids the charity and ‘Supporter’ is the person who will support your charity financially or non-financially or preferably both!

banner-newBy recruiting the long term Donors as reliable supporters for more duties assigned to them is important. Charity needs to ask its Donors for their maximum time than money , as it can increase their giving to the charity. Donors need to be attracted. There could be ways in which  donors could be drawn by  direct marketing techniques like: face 2 face talking, cold-telemarketing, SMS (whatsapp) activity which has proven to be 18% higher  than other strategies. There is a need to create lifetime values.

Databases of the donors, created with their updated achievements and contributions is a good practice along with legal compliance. By ‘appreciating’ what the Donors are giving to the non-profit organisation can boost their moral. Lets say, if they donate 100 GBP to the charity , they should feel good about it and charity should make sure their contributions are ‘Valued’ . A thank you note on how the charity has been benefitted can strengthen the relationship between the Donors and the charity. ‘People get more interested with acknowledgement and accreditation’.In

The ‘Retention‘ of the existing Donors can be done by engaging them actively for the cause of the existing charity ,let alone the money factor. Inducing the enthusiasm for ‘philanthropic support’ of wealthy individuals or corporate decision makers can work wonders. Donors who are easier to recruit and who can stay longer, charity has to start thinking of them as ‘supporters’ instead.Major Donors can provide in-kind support, at-cost services, expertise or at as influential Ambassadors for the charity.1217_charity-donation_1200x675

Furthermore, the first 4-5 months donors stay digitally active and also the ‘Attrition rate‘ of the donors who don’t join the network of charity’s group is 20% higher on average.

To create more loyalty amongst the modern day digitally-hopping donors, value creation by making them aware of the latest events of the charity and by sending them Newsletters can keep them engaged.More communication based campaigns with creative inputs should run, with important information highlighted in it. The efficient use of the existing database of Donors can be helpful to start conversations with them in order to make deep-rooted relationships and convert them to supporters.

The database security is crucial in order to fundraise  , set clear objectives , schedule the meetings, map the potential supporter journey for all the contacts. Which are the potential Donors who can devote themselves with extra responsibility to the charity.By weekly scheduling the meetings to see the number of conversion rate of Donors into supporters and what all measures were taken. So, mapping each journey of how an individual Donor became a regular supporter, what activities of interests he/she took up, what key areas were of utmost requirement and how they were approached. How the team building activities(ice-breakers, formal introductions, team lunches) are undertaken between the old supporters, Donors and the new ones. As in the end of the day a wholesome environment has to be provided for the Donor as he can be assessing the Charity’s ability to sustain in long run.



Data scaling and Digital Profiles

This was a brilliant talk @outreachdigit on how to build data teams with Florian Douetteau @dataiku  and how to overcome various technological and HR related challenges faced.

He touched upon the topics like how artificial intelligence is crucial for the data for internal things. How the predictive analytics can work , The Deployment strategy can be useful. How one can push new behaviours of users.

Classical businesses have an intellectual team to perform their data analytics. Nowadays, specialised workforce is recruited in for web companies. What happens to the data which is generated by every click or a tweet. It has to be managed by really niche profiles of people with  career paths like Data analysts, Data scientist and Data engineers.

Data Analysts: They have a strong understanding of how to use existing tools to solve the problems with core competencies in programming, stats, machine learning, data munging and data visualisation. They have to present data analysis effectively.

Rising alongside the relatively new technology of big data is the need of new job title called a Data scientist. A data scientist represents an evolution from the business or data analyst role. Good data scientists will not just address business problems, they will pick the right problems that have the most value to the organization. They look at the data from many angles for developing data, by firstly determining what it means and classifying it. then recommends ways to apply the data which is implementation.

The data scientist role has been described as “part analyst, part artist.”

Then the Data engineers: A data engineer builds a robust, fault-tolerant data pipeline that cleans, transforms, and aggregates unorganized and messy data into databases or datasources. Data engineers are typically software engineers by trade. Instead of data analysis, data engineers are responsible for compiling and installing database systems, writing complex queries, scaling to multiple machines, and putting disaster recovery systems into place.

Data engineers essentially lay the groundwork for a data analyst or data scientist to easily retrieve the needed data for their evaluations and experiments.

So, profiles for different types of data they usually have varying goals-analytics behaviour of different segments. Main goal is to deliver human team dynamics. It is very important to hire the right workforce to manage the data.

Image via Data Sciencdata-scientist-vs-data-engineer.jpge 101

Clickers and Coders -Ages ago more people were coding like Ms Dos etc whereas people now have become more clickers, especially millennials only know how to click and drags on the touch screens,

Data issues: How are they supposed to get data inside his data lake? Which strategy should they adopt: the cicada, the spider or the fox one? There are different data strategies that are used like Cicada strategy where a startup can build new product using open data.

Cicada Strategy is when open data is freely accessible. He trusts open data, current or future, in order to provide his service.

This Open Data strategy can yield profitable results in the financial or transport markets; for example, startups can use merchandise transportation information and cross reference it with information on cargo and market prices, to provide highly relevant information to industry professionals.

The main drawback of the Open Data approach is the limited scope of open data. Indeed, for both ethical and economic reasons (which come together for once) open data is lacking when you are looking to learn specific things about a person, a product or an address… Anyway, the most useful things are private (fortunately) and paid (unfortunately)

Spider strategy is meticulous network of web trackers. Spider is a network of points where one can go to capture the data in every possible manner, sometimes starting with the smallest, and then gradually looks for the bigger ones. The spider will manufacture all the access points, all the connectors, allowing each player to provide him with its data and use his service.

Most online marketers take this approach: this means having your “tracker” (component for capturing traffic from a third party site) all over the web, so as to have the most data and the largest network possible.

Fox strategy: is a very good strategy for startups.

The fox seeks out “Big Data” where it is: in large businesses where “Big Data” is well fed! with this strategy business groups can take charge of critical problem and then sell the model to other companies.They build their own integrated problem within the projects for which they get fundings to solve critical situations. Just like a fox by first suggesting a possible solution to a problem. ◦ e.g., reducing your fraud, improving your ad buy costs, increasing the performance of your email marketing programs, optimizing the cost of raw material purchases, etc., etc.)

Thus, the knowledge obtained from this first customer to simply solve the problems of other customers.

The fox has a difficult life, because in order for his first approach to succeed, he must make believe he can solve a problem that he’s never solved before! To do this, he must stir the desires of the powerful (charming the big bosses of the group), flaunt his power.

Product issues: What is big data really about? And eventually, what are they willing to do with this bunch of data   We live in age of distributed intelligence  like I-cloud.  The product teams must keep focus of the data. With high amount of artificial intelligence permeating in the sales and marketing scenario where automated emailers, newsletters, chat replies are sent to the customers, huge regulations have to be considered. For analysing the continuos prospect behaviours on social networks where CRM systems can joke and make decisions.There could also be data breach, privacy hindrance and legitimacy. Companies like Amazon, Linkedin use highly data smart softwares.