Lean Startup in big corporations - Lean Startup Machine Talk

Lean Startup and innovation in
big corporations
How experimentations helped us align dispersed
teams, and assert our assumptions fast.
@benoitguillou@patothon
LS is about Customer
Development
& Product Development
but we strongly believe
it’s worthless without
discipline, rigor, and
processes
It needs time and follow up, on everything,
from the sales pipe to the feature releases.
We want small lead times, cycle time and little
waste.
Why? To assert our business assumptions fast.
So, what are the main
problems when innovating in
big corporations?
Ticketing culture and the loss of ownership,
empowerment.
It's like nobody is never responsible for anything.
No follow up. No measurement. No lessons.
No improvements.
We had those problems.
Well, we still have them.
But we managed to manage them.
We built a team with the minimum set of skills
to tackle the problems.
No follow up. No measurement. No lessons.
No improvements.
[ex·per·i·ment] (/ikˈsperəmənt/) - noun
"A scientific procedure undertaken to make a discovery, test a hypothesis, or
demonstrate a known fact."
Synonyms : trial - test - try - attempt - essay - assay - tentative
Everything started with what we called
lessons learned. It was simple slide decks
containing shareable information, as metric
reports and objectives.
Hypothèse
Les candidats qui reviennent vont jusqu’au bout
MesureConclusion
1/3 de la base est réutilisable.
=> Effet déceptif + Profil déjà périmé ?
Mail On site
Open Rate
30 % 55%
Mails
240 240
Conversion rate
Tunnel
d’inscription fini
50% 40%
Visits
35 79
Bounce
30% 25%
Click rate
40 % 51%
Indicateur
Prévu Mesuré
28/03
We needed something better, as shareable,
but with more information, like the context,
the big picture solution, the actions taken,
the metric following, and still, the lessons
learned. We called this one
“experimentation”.
Expérimentations en cours
Contexte (Problème, Risque, Idée)
Acquisition SEM / SEO CANDIDAT RECRUTEUR
quali quanti
Opportunités
DMR non faite par recruteur
10% des candidats avec des offres
 Envoyer des opportunités aux candidats
CANDIDAT RECRUTEUR
quali quanti
Nouvelles offres commerciales
Offre startup (com’ & positionnement produit)
Offre essai
Offre de stage  Offre cab
RECRUTEUR
quali quanti
Commerciaux offline
Pas de réussites partagées
problème de confiance dans la solution
manque d’info au niveau des commerciaux, manque de feedback venant des commerciaux
RECRUTEUR
quali quanti
Commerciaux online
1 seul canal d’acquisition commercial, Commerciaux Offline
Manque d’offres, problème de confiance dans la solution
 Développement du canal Offline
RECRUTEUR
quali quanti
Conférence + Matchmaking
Acquisition candidat + image de marque
Matchmaking / Conférence dans les écoles, les salons
CANDIDAT RECRUTEUR
quali quanti
Page 2
Page 3
Page 4
Page 5
Page 6
Page 7
Plan de contact
Base inactive / qualité des profils
RECRUTEUR
quali quanti
Page 8
Experimentation
Responsable : Vincent
Contributor(s) : Benoît Informed(s):
Context (Problem, Risk, Idea)
Envoyer offre en mode pro-actif
Pb : DMR non faite par recruteur, 10% des candidats avec des offres
Lessons learned
Les annonces Web Tech marchent très bien
Toulouse / Paris en tête
Les opportunités marchent mieux que les DMR.
Les candidats ne sont pas recontactés
Les recruteurs n’arrivent pas forcément à contacter les candidats.
Solution Big Picture
Envoie des matchings à 400 candidats
Si ils sont intéressés, on les propose aux recruteurs
Goals
Au 15/01
Candidat avec une offre
suggérée: 30%
Candidats intéressés : 400
Maiwenn : Tx d’ouverture
improve_profile 40%
Tx Rebond après improve
profile : max 20%
Current Metrics
434 dmr acceptées / 433
intéressées (7/02)
2079 acceptées dont 642 dmr
acceptées / 1437 intéressées
(18/03)
Maiwenn Tx d’ouverture
improve_profile 49%; (11/04)
+5%
Tx d’ouverture doublé (11/04)
Actions
• Suggestion automatique
• Plan de relance pour les opportunités automatisé
• Suggestion sur le profil public
• Améliorer le mail d’amélioration de profil
• Rediriger vers edition profil
• Template + objet
• Suggérer au candidat qui n’ont pas encore eu d’offres
The last thing we tried, that definitely is a
complement of the previous version, takes
into account the lack of follow up. It’s what
we called experiments. And it worked for us,
and definitely changed the way we worked
as a team.
Lean Startup in big corporations - Lean Startup Machine Talk
Lean Startup in big corporations - Lean Startup Machine Talk
We decided to organize our team around
“lessons learned” and “experiments”. And we
built the tools to support this new organization.
The measurable aspect of those experiments
gave ownership to people working on the
project.
The testable aspect helped us focus and align
the team around credible and written goals.
Follow experiments through time, weeks,
sometimes months helped us empower people
and make them accountable and responsible
for the whole product.
This was a long process 9 months.
We iterated. As everything in LS, we
explored, we pivoted to find what was right
for us.
Annex question : is the journey or the result what made our team
stronger?
( you have 4 hours ;) )
http://www.experiments.io
1 of 26

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Lean Startup in big corporations - Lean Startup Machine Talk

  • 1. Lean Startup and innovation in big corporations How experimentations helped us align dispersed teams, and assert our assumptions fast.
  • 3. LS is about Customer Development & Product Development but we strongly believe it’s worthless without discipline, rigor, and processes
  • 4. It needs time and follow up, on everything, from the sales pipe to the feature releases.
  • 5. We want small lead times, cycle time and little waste. Why? To assert our business assumptions fast.
  • 6. So, what are the main problems when innovating in big corporations?
  • 7. Ticketing culture and the loss of ownership, empowerment. It's like nobody is never responsible for anything.
  • 8. No follow up. No measurement. No lessons. No improvements.
  • 9. We had those problems.
  • 10. Well, we still have them.
  • 11. But we managed to manage them.
  • 12. We built a team with the minimum set of skills to tackle the problems.
  • 13. No follow up. No measurement. No lessons. No improvements.
  • 14. [ex·per·i·ment] (/ikˈsperəmənt/) - noun "A scientific procedure undertaken to make a discovery, test a hypothesis, or demonstrate a known fact." Synonyms : trial - test - try - attempt - essay - assay - tentative
  • 15. Everything started with what we called lessons learned. It was simple slide decks containing shareable information, as metric reports and objectives.
  • 16. Hypothèse Les candidats qui reviennent vont jusqu’au bout MesureConclusion 1/3 de la base est réutilisable. => Effet déceptif + Profil déjà périmé ? Mail On site Open Rate 30 % 55% Mails 240 240 Conversion rate Tunnel d’inscription fini 50% 40% Visits 35 79 Bounce 30% 25% Click rate 40 % 51% Indicateur Prévu Mesuré 28/03
  • 17. We needed something better, as shareable, but with more information, like the context, the big picture solution, the actions taken, the metric following, and still, the lessons learned. We called this one “experimentation”.
  • 18. Expérimentations en cours Contexte (Problème, Risque, Idée) Acquisition SEM / SEO CANDIDAT RECRUTEUR quali quanti Opportunités DMR non faite par recruteur 10% des candidats avec des offres  Envoyer des opportunités aux candidats CANDIDAT RECRUTEUR quali quanti Nouvelles offres commerciales Offre startup (com’ & positionnement produit) Offre essai Offre de stage  Offre cab RECRUTEUR quali quanti Commerciaux offline Pas de réussites partagées problème de confiance dans la solution manque d’info au niveau des commerciaux, manque de feedback venant des commerciaux RECRUTEUR quali quanti Commerciaux online 1 seul canal d’acquisition commercial, Commerciaux Offline Manque d’offres, problème de confiance dans la solution  Développement du canal Offline RECRUTEUR quali quanti Conférence + Matchmaking Acquisition candidat + image de marque Matchmaking / Conférence dans les écoles, les salons CANDIDAT RECRUTEUR quali quanti Page 2 Page 3 Page 4 Page 5 Page 6 Page 7 Plan de contact Base inactive / qualité des profils RECRUTEUR quali quanti Page 8
  • 19. Experimentation Responsable : Vincent Contributor(s) : Benoît Informed(s): Context (Problem, Risk, Idea) Envoyer offre en mode pro-actif Pb : DMR non faite par recruteur, 10% des candidats avec des offres Lessons learned Les annonces Web Tech marchent très bien Toulouse / Paris en tête Les opportunités marchent mieux que les DMR. Les candidats ne sont pas recontactés Les recruteurs n’arrivent pas forcément à contacter les candidats. Solution Big Picture Envoie des matchings à 400 candidats Si ils sont intéressés, on les propose aux recruteurs Goals Au 15/01 Candidat avec une offre suggérée: 30% Candidats intéressés : 400 Maiwenn : Tx d’ouverture improve_profile 40% Tx Rebond après improve profile : max 20% Current Metrics 434 dmr acceptées / 433 intéressées (7/02) 2079 acceptées dont 642 dmr acceptées / 1437 intéressées (18/03) Maiwenn Tx d’ouverture improve_profile 49%; (11/04) +5% Tx d’ouverture doublé (11/04) Actions • Suggestion automatique • Plan de relance pour les opportunités automatisé • Suggestion sur le profil public • Améliorer le mail d’amélioration de profil • Rediriger vers edition profil • Template + objet • Suggérer au candidat qui n’ont pas encore eu d’offres
  • 20. The last thing we tried, that definitely is a complement of the previous version, takes into account the lack of follow up. It’s what we called experiments. And it worked for us, and definitely changed the way we worked as a team.
  • 23. We decided to organize our team around “lessons learned” and “experiments”. And we built the tools to support this new organization.
  • 24. The measurable aspect of those experiments gave ownership to people working on the project. The testable aspect helped us focus and align the team around credible and written goals. Follow experiments through time, weeks, sometimes months helped us empower people and make them accountable and responsible for the whole product.
  • 25. This was a long process 9 months. We iterated. As everything in LS, we explored, we pivoted to find what was right for us. Annex question : is the journey or the result what made our team stronger? ( you have 4 hours ;) )

Editor's Notes

  1. Who are we ? built 2 startups, failed one, one side, one building.Trying to use Lean Startup practices since 3 years, after reading Running Lean.Right now : building chooseyourboss as intrapreneurs, building tools for product managers.Important : we are not ayatollahs. At beginning, we were like “monkey see, monkey do”, now we’re a little more souple =) What is important to understand is that every context is different, every market, every product is different, so what we managed to do doesn’t necessarily apply to you. But we believe that there are some lessons here. So don’t forget : there is no “absolute truth”. Lean Startup just like Agile or Lean is a way of thinking, a discipline, not predefined rules to make you successful or not if you don’t follow them.
  2. What we strongly believe in, after 3 years of practice in our own ventures, and also as product manager in big companies.
  3. From the idea to the dollars, or at least the lessonslearned
  4. As Lean intrapreneurs/Product Managers what do we want?Reduce the market risks
  5. It is important to note that we started, at the really beginning, to build a minimum viable team, with all the skills needed to implement the product, from business to engineering
  6. We needed to align this team around common measurable goals, and make them follow those ones, even after the “ticket” is “done”.We needed to kill this “DONE” paradigm. And give birth to a new “done
  7. What we gained here?The measurable aspect of those experiments gave ownership to people working on the project. They understood that their actions will be checked, and a ticket done was ok, but done needed to be “KPI compliant”.Problem, no following, no improvements, but high power inshareability and understanding, + + lessons learned. A lot of informations were needed. Here for example, the marketing was looking at us saying : what was done exactly? It was more, by us, for us. NOT GOOD YET =)
  8. This was the slide we were sharing to everyone every Thursday, available on dropbox. This was the high level view, with : The problems we were solvingThe segmentThe type of experimentation (quali/quanti)
  9. What we gained here?Here, there is an indeniable testable aspect. Expliciting clearly the problem, having to write in a small box the big picture solution, and also, the mesurable goals made the people think small, testable and try things differently. This aspect helped us focus and align the team around credible and written goalsThe main problem here, is that it was definitely lacking follow up, and be able to understand what action change what metric sometimes. Plus, this was still too clunky.In ticketing organisations, one day you talk to someone, one other day to another one, for a various number of reasons. And while some experimentations were lasting weeks/months for some of them, people were coming back and saying we should do something we already did weeks back, and we were on for explanations why we wouldn’t do that, but without the “data proofs”, and the lessons learned of that time, which would have been seriously helpful.
  10. What we gained here?The fact that you need to follow those “experiments” through time, weeks, sometimes months helped us empower people and make them accountable and responsible for the whole product. They understood that we were iterating on everything, constantly trying to improve, or kill, or create new things that would bring value to the customer. They were definitely still in the ticketing culture, but for operational purposes (who does what), but not for product management anymore.This definitely helped us to implicate the marketing way more than the other solutions, because anybody could come on the experiment and understand what happened, through time, and quite easily. Every action was linked to an incomeThis is not only actions on the mail but on the product itselfWe follow this mail + the landing + the high metrics > everything was shared with the sales to build up powerful presales speech for instance
  11. Conlusion
  12. What we achieved?
  13. Butdon’t forget =)