what to do with data

15
What to do with Data? Doing stuff with my team minimums and productivity data.

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Page 1: What to do with data

What to do with Data?Doing stuff with my team minimums and

productivity data.

Page 2: What to do with data

There is a gap between the collection of data about our members and

doing something about it.

of VPTMs are not aware of their local committee through the use

of data analytics

61%

of VPTMs believe they have the trust of their EB/LCP to manage

the TMP/TLP for RA-MA-RE

50%

Page 3: What to do with data

that ’ s okay let ’ s s tart from the beg inn i ng

step one:set productivity

goals

Page 4: What to do with data

productivity goals:you already have them defined within your programs eg. iGIP

Productivity of ‘5’ for 6 months. Now: 1. Break down that productivity goal for November and December

eg. iGIP RA goal for November is 50, I have 30 iGIP TMP doing raising, therefore my productivity RA goal for Nov iGIP = 1.6 (aka. each member does 1.6)

2. Since it’s small consider breaking it down further: Since it takes 5 meetings to get one iGIP RA 1.6 RA Productivity = 7.5 meetings productivity (aka. each iGIP member does 7.5 meetings in November)

Page 5: What to do with data

track what is easy for you!

Page 6: What to do with data

th i s can be week ly or

month ly

step two:track

productivity goals

Page 7: What to do with data

tracking:for example you have a November iGIP Productivity goal of 1.2 RA

(aka. each raising iGIP member does 1.2 raises)

This then breaks down to 6 meetings (5 meetings: 1 raise) and then further breaks down to 18 cold calls (1 meeting: 3 calls).

So, 18 cold calls a month per member or 4.5 calls per week.

“as VPTM I have this data, my HR responsibles/ team/ VPs/

MB are tracking it” H e re a r e t h e r e s u l t s : W 1 : 3 c a l l s W 2 : 1 c a l l W 3 : 0 . 4 c a l l s W 4 : 0 c a l l s

My November productivity = 0.28 RA

Page 8: What to do with data

what happened? :(

Page 9: What to do with data

th i s i s where your

data ana lyt i cs i s

requ i red !

step three:interpret productivity weekly and convert to

actionwhen you see productivity is low there

are usually three problems: 1. Member don’t know want to work 2. Members don’t know how to work 3. There’s too much work

aka. 1. Team/Leadership 2. Learning and Development 3. Capacity

Page 10: What to do with data

interpreting:

follow up all your data analytics with

personal chats/ conversations

aka. 1. Team/Leadership 2. Learning and Development 3. Capacity

1. Check your Team Minimums data & follow up with personal chats of the MB/ TMPs of the team. what is missing in the team minimums data? maybe there’s not team purpose or no coaching chats. 2. Check your Learning and Development Plan How much of the LnD plan has been delivered for this team? 3. Check the team retention - does this team have enough people?

Page 11: What to do with data

got it? kay.

let’s get more advanced.

*not needed for membership less than 80 TMP

Page 12: What to do with data

using performance vs. potential

step 1:Check you have and are collecting the right data. So that you can accurately plot an individual’s spot on the performance vs. potential curve. a) Performance: eg. Performance Appraisals, coaching chats, tracking

minimum KPIs b) Potential: eg. Competency Assessment Tool, Personal Assessments,

‘Exceeds Expectations - Unsatisfactory’ Rating Scales.

step 2:Plot and categorise (with flexibility) your members into the curve. Recognise clusters and interpret data.

Page 13: What to do with data

where do your employees fall?

Check this example from Bank of America that grades their employees based on performance vs. potential.

Key Talent: are pipelined and give opportunities for further challenges and development. Top-grading opportunities: are given immediate 30-90 day action plans to improve performance. Leadership Issues: are given immediate coaching.

Page 14: What to do with data

step 3:Take action: This action can be based on the cluster/category that they have been identified in. example given in the previous page and below.

1. recognise/ recommend pipeline 2. understand the skills/

competencies they need to develop to get there.

3. make action steps on how to develop those competencies.

Page 15: What to do with data

simple yeah? :)